- one_file_per_timestepFalseCreate a unique output file for each time step of the simulation.
Default:False
C++ Type:bool
Controllable:No
Description:Create a unique output file for each time step of the simulation.
- use_displacedFalseEnable/disable the use of the displaced mesh for outputting
Default:False
C++ Type:bool
Controllable:No
Description:Enable/disable the use of the displaced mesh for outputting
- use_legacy_reporter_outputFalseUse reporter output that does not group by object.
Default:False
C++ Type:bool
Controllable:No
Description:Use reporter output that does not group by object.
JSON
The JSONOutput object exists to output values from the Reporter System in the JavaScript Object Notation (JSON) format. Reporter values are arbitrary types, as such the JSON output supports the output of any type if the correct output helper function exists.
Example
The following input snippet demonstrates the default method for enabling JSON output.
[Outputs]
json = true
[]
The resulting JSON output is generally in the following form, where the time information are provided at the top level and reporters values are nested within a list for the time steps.
{
"reporters": {
"iteration_info": {
"type": "IterationInfo",
"values": {
"time": {
"type": "double"
}
}
}
},
"time_steps": [
{
"iteration_info": {
"time": 0.0
},
"time": 0.0,
"time_step": 0
},
{
"iteration_info": {
"time": 1.0
},
"time": 1.0,
"time_step": 1
},
{
"iteration_info": {
"time": 2.0
},
"time": 2.0,
"time_step": 2
},
{
"iteration_info": {
"time": 3.0
},
"time": 3.0,
"time_step": 3
}
]
}
Distributed Output
If a Reporter value is computed with REPORTER_MODE_DISTRIBUTED (see Reporter System a JSON file for each process will automatically be created with the distributed data and the total number of parts and part number for the file will be included in the output.
to_json function
In similar fashion to the DataIO functions used for restart and recovery a to_json method must exist for a type to be supported for output to a JSON file. The function is defined in the nlohmann/json library—the library relied upon by MOOSE for JSON support—for this purpose.
For example, the following code snippets show the declaration and definition of the to_json method for the MooseApp object.
void to_json(nlohmann::json & json, const MooseApp & app);
void
to_json(nlohmann::json & json, const MooseApp & app)
{
if (!app.getSystemInfo())
return;
json["app_name"] = app.name();
json["current_time"] = app.getSystemInfo()->getTimeStamp();
json["executable"] = app.getSystemInfo()->getExecutable();
json["executable_time"] = app.getSystemInfo()->getExecutableTimeStamp(json["executable"]);
json["moose_version"] = MOOSE_REVISION;
json["libmesh_version"] = LIBMESH_BUILD_VERSION;
#ifdef LIBMESH_DETECTED_PETSC_VERSION_MAJOR
json["petsc_version"] = std::to_string(LIBMESH_DETECTED_PETSC_VERSION_MAJOR) + "." +
std::to_string(LIBMESH_DETECTED_PETSC_VERSION_MINOR) + "." +
std::to_string(LIBMESH_DETECTED_PETSC_VERSION_SUBMINOR);
#endif
#ifdef LIBMESH_DETECTED_SLEPC_VERSION_MAJOR
json["slepc_version"] = std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_MAJOR) + "." +
std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_MINOR) + "." +
std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_SUBMINOR);
#endif
}
Input Parameters
- additional_execute_onThe list of flag(s) indicating when this object should be executed, the available options include FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM.
C++ Type:ExecFlagEnum
Options:FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM
Controllable:No
Description:The list of flag(s) indicating when this object should be executed, the available options include FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM.
- execute_onINITIAL TIMESTEP_ENDThe list of flag(s) indicating when this object should be executed, the available options include FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM.
Default:INITIAL TIMESTEP_END
C++ Type:ExecFlagEnum
Options:FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM
Controllable:No
Description:The list of flag(s) indicating when this object should be executed, the available options include FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM.
- output_linearFalseSpecifies whether output occurs on each PETSc linear residual evaluation
Default:False
C++ Type:bool
Controllable:No
Description:Specifies whether output occurs on each PETSc linear residual evaluation
- output_nonlinearFalseSpecifies whether output occurs on each PETSc nonlinear residual evaluation
Default:False
C++ Type:bool
Controllable:No
Description:Specifies whether output occurs on each PETSc nonlinear residual evaluation
Execution Scheduling Parameters
- append_dateFalseWhen true the date and time are appended to the output filename.
Default:False
C++ Type:bool
Controllable:No
Description:When true the date and time are appended to the output filename.
- append_date_formatThe format of the date/time to append, if not given UTC format is used (see http://www.cplusplus.com/reference/ctime/strftime).
C++ Type:std::string
Controllable:No
Description:The format of the date/time to append, if not given UTC format is used (see http://www.cplusplus.com/reference/ctime/strftime).
- file_baseThe desired solution output name without an extension. If not provided, MOOSE sets it with Outputs/file_base when available. Otherwise, MOOSE uses input file name and this object name for a master input or uses master file_base, the subapp name and this object name for a subapp input to set it.
C++ Type:std::string
Controllable:No
Description:The desired solution output name without an extension. If not provided, MOOSE sets it with Outputs/file_base when available. Otherwise, MOOSE uses input file name and this object name for a master input or uses master file_base, the subapp name and this object name for a subapp input to set it.
- output_if_base_containsIf this is supplied then output will only be done in the case that the output base contains one of these strings. This is helpful in outputting only a subset of outputs when using MultiApps.
C++ Type:std::vector<std::string>
Controllable:No
Description:If this is supplied then output will only be done in the case that the output base contains one of these strings. This is helpful in outputting only a subset of outputs when using MultiApps.
- padding4The number of digits for the extension suffix (e.g., out.e-s002)
Default:4
C++ Type:unsigned int
Controllable:No
Description:The number of digits for the extension suffix (e.g., out.e-s002)
File Name Customization Parameters
- control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
- enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Controllable:Yes
Description:Set the enabled status of the MooseObject.
Advanced Parameters
- end_stepTime step at which this output object stop operating
C++ Type:int
Controllable:No
Description:Time step at which this output object stop operating
- end_timeTime at which this output object stop operating
C++ Type:double
Controllable:No
Description:Time at which this output object stop operating
- interval1The interval at which time steps are output to the solution file
Default:1
C++ Type:unsigned int
Controllable:No
Description:The interval at which time steps are output to the solution file
- minimum_time_interval0The minimum simulation time between output steps
Default:0
C++ Type:double
Controllable:No
Description:The minimum simulation time between output steps
- start_stepTime step at which this output object begins to operate
C++ Type:int
Controllable:No
Description:Time step at which this output object begins to operate
- start_timeTime at which this output object begins to operate
C++ Type:double
Controllable:No
Description:Time at which this output object begins to operate
- sync_onlyFalseOnly export results at sync times
Default:False
C++ Type:bool
Controllable:No
Description:Only export results at sync times
- sync_timesTimes at which the output and solution is forced to occur
C++ Type:std::vector<double>
Controllable:No
Description:Times at which the output and solution is forced to occur
- sync_times_objectTimes object providing the times at which the output and solution is forced to occur
C++ Type:TimesName
Controllable:No
Description:Times object providing the times at which the output and solution is forced to occur
- time_tolerance1e-14Time tolerance utilized checking start and end times
Default:1e-14
C++ Type:double
Controllable:No
Description:Time tolerance utilized checking start and end times
Timing And Frequency Of Output Parameters
- execute_reporters_onControl of when Reporter values are output
C++ Type:ExecFlagEnum
Options:FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM
Controllable:No
Description:Control of when Reporter values are output
- execute_system_information_onINITIALControl when the output of the simulation information occurs
Default:INITIAL
C++ Type:ExecFlagEnum
Options:FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, FINAL, FAILED, CUSTOM
Controllable:No
Description:Control when the output of the simulation information occurs
- hideA list of the variables and postprocessors that should NOT be output to the Exodus file (may include Variables, ScalarVariables, and Postprocessor names).
C++ Type:std::vector<VariableName>
Controllable:No
Description:A list of the variables and postprocessors that should NOT be output to the Exodus file (may include Variables, ScalarVariables, and Postprocessor names).
- showA list of the variables and postprocessors that should be output to the Exodus file (may include Variables, ScalarVariables, and Postprocessor names).
C++ Type:std::vector<VariableName>
Controllable:No
Description:A list of the variables and postprocessors that should be output to the Exodus file (may include Variables, ScalarVariables, and Postprocessor names).
Selection/Restriction Of Output Parameters
- linear_residual_dt_divisor1000Number of divisions applied to time step when outputting linear residuals
Default:1000
C++ Type:double
Controllable:No
Description:Number of divisions applied to time step when outputting linear residuals
- linear_residual_end_timeSpecifies an end time to begin output on each linear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies an end time to begin output on each linear residual evaluation
- linear_residual_start_timeSpecifies a start time to begin output on each linear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies a start time to begin output on each linear residual evaluation
- nonlinear_residual_dt_divisor1000Number of divisions applied to time step when outputting non-linear residuals
Default:1000
C++ Type:double
Controllable:No
Description:Number of divisions applied to time step when outputting non-linear residuals
- nonlinear_residual_end_timeSpecifies an end time to begin output on each nonlinear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies an end time to begin output on each nonlinear residual evaluation
- nonlinear_residual_start_timeSpecifies a start time to begin output on each nonlinear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies a start time to begin output on each nonlinear residual evaluation
Petsc Linear/Nonlinear Output Parameters
- postprocessors_as_reportersFalseOutput Postprocessors values as Reporter values.
Default:False
C++ Type:bool
Controllable:No
Description:Output Postprocessors values as Reporter values.
- vectorpostprocessors_as_reportersFalseOutput VectorsPostprocessors vectors as Reporter values.
Default:False
C++ Type:bool
Controllable:No
Description:Output VectorsPostprocessors vectors as Reporter values.
Conversions Before Output Parameters
Input Files
- (modules/stochastic_tools/test/tests/reporters/statistics/statistics.i)
- (modules/stochastic_tools/test/tests/reporters/mapping/map_main.i)
- (modules/stochastic_tools/test/tests/reporters/BFActiveLearning/main_adam.i)
- (test/tests/reporters/nodal_reporter/nodal_stats.i)
- (modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss.i)
- (test/tests/positions/reporter_positions.i)
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_batch.i)
- (modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromInput.i)
- (modules/stochastic_tools/test/tests/reporters/basic/statistics_vpp.i)
- (modules/stochastic_tools/test/tests/reporters/basic/statistics_vpp_ci.i)
- (modules/stochastic_tools/examples/parameter_study/main.i)
- (modules/combined/examples/stochastic/lhs_uniform.i)
- (test/tests/meshgenerators/mesh_repair_generator/overlapping_fix_test.i)
- (test/tests/reporters/mesh_meta_data_reporter/mesh_meta_data_reporter.i)
- (test/tests/reporters/constant_reporter/constant_reporter.i)
- (modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter_error.i)
- (test/tests/controls/libtorch_nn_control/read_control.i)
- (test/tests/utils/shuffle/shuffle.i)
- (modules/stochastic_tools/test/tests/userobjects/inverse_mapping/create_mapping_main.i)
- (modules/stochastic_tools/test/tests/reporters/statistics/statistics_distributed.i)
- (modules/optimization/test/tests/optimizationreporter/parameter_mesh_base/paramMeshOptRep.i)
- (modules/stochastic_tools/test/tests/samplers/mcmc/main_des.i)
- (modules/stochastic_tools/test/tests/likelihoods/gaussian_derived/main.i)
- (test/tests/transfers/multiapp_reporter_transfer/main.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_main.i)
- (test/tests/transfers/multiapp_reporter_transfer/sub0.i)
- (test/tests/meshgenerators/parsed_element_deletion_generator/cut_in_half.i)
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_small.i)
- (test/tests/positions/element_centroid_positions.i)
- (modules/stochastic_tools/test/tests/transfers/serialized_solution_transfer/sst_main.i)
- (test/tests/times/time_interval_times.i)
- (test/tests/reporters/perf_graph_reporter/perf_graph_reporter.i)
- (test/tests/reporters/iteration_info/iteration_info_steady.i)
- (test/tests/outputs/hide_via_reporters_block/reporter.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_locs.i)
- (test/tests/times/input_times.i)
- (modules/stochastic_tools/test/tests/multiapps/dynamic_sub_app_number/main.i)
- (modules/stochastic_tools/test/tests/variablemappings/pod_mapping/pod_mapping_main.i)
- (test/tests/times/reporter_times.i)
- (test/tests/reporters/declare_initial_setup/declare_initial_setup.i)
- (test/tests/positions/distributed_positions.i)
- (modules/stochastic_tools/test/tests/reporters/mapping/load_main.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_normal.i)
- (modules/stochastic_tools/test/tests/reporters/morris/morris_main.i)
- (test/tests/transfers/multiapp_reporter_transfer/sub1.i)
- (test/tests/reporters/element_reporter/elem_stats.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_uniform.i)
- (modules/stochastic_tools/test/tests/samplers/mcmc/main_ss.i)
- (test/tests/positions/file_positions.i)
- (modules/stochastic_tools/examples/parameter_study/main_vector.i)
- (modules/stochastic_tools/test/tests/samplers/mcmc/main_base.i)
- (test/tests/reporters/restartable_data_reporter/restartable_data_reporter.i)
- (test/tests/transfers/multiapp_reporter_transfer/clone.i)
- (modules/stochastic_tools/test/tests/surrogates/polynomial_regression/poly_reg_vec.i)
- (modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_surr.i)
- (modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_surr.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca.i)
- (test/tests/positions/input_positions.i)
- (modules/stochastic_tools/examples/parameter_study/main_time.i)
- (modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss_error1.i)
- (test/tests/outputs/vpp_as_reporter/vpp_as_reporter.i)
- (modules/stochastic_tools/test/tests/multiapps/conditional_run/conditional_main.i)
- (modules/stochastic_tools/test/tests/reporters/sobol/sobol_main.i)
- (test/tests/outputs/json/one_file_per_timestep/json.i)
- (modules/stochastic_tools/test/tests/transfers/libtorch_nn_transfer/libtorch_drl_control_sub.i)
- (modules/stochastic_tools/examples/surrogates/cross_validation/all_trainers_uniform_cv.i)
- (test/tests/reporters/extra_id_integral/extra_id_integral.i)
- (test/tests/times/exodus_times.i)
- (modules/reactor/test/tests/positions/cartesian_grid_positions.i)
- (test/tests/reporters/accumulated_reporter/accumulate_reporter.i)
- (modules/stochastic_tools/test/tests/surrogates/cross_validation/poly_reg_vec.i)
- (test/tests/reporters/declare_initial_setup/declare_initial_setup_with_get.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_moment.i)
- (test/tests/reporters/mesh_info/mesh_info.i)
- (test/tests/times/functor_times.i)
- (modules/combined/examples/stochastic/poly_chaos_uniform.i)
- (test/tests/meshgenerators/parsed_element_deletion_generator/cut_the_small.i)
- (modules/optimization/test/tests/optimizationreporter/parameter_mesh_base/twoParamMeshOptRep.i)
- (test/tests/times/simulation_times.i)
- (modules/stochastic_tools/test/tests/reporters/stochastic_reporter/stats.i)
- (modules/stochastic_tools/examples/sobol/main.i)
- (test/tests/misc/solution_invalid/solution_invalid.i)
- (modules/stochastic_tools/test/tests/reporters/statistics/statistics_main.i)
- (modules/stochastic_tools/test/tests/surrogates/load_store/evaluate.i)
- (test/tests/positions/creating_multiapps/apps_from_positions.i)
- (modules/stochastic_tools/test/tests/surrogates/nearest_point/np_vec.i)
- (modules/stochastic_tools/test/tests/transfers/libtorch_nn_transfer/libtorch_drl_control_trainer.i)
- (test/tests/reporters/perf_graph_reporter/perf_graph_reporter_recover.i)
- (modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_groupBounds.i)
- (test/tests/positions/transformed_positions.i)
- (modules/stochastic_tools/test/tests/reporters/ActiveLearningGP/main_adam.i)
- (test/tests/meshgenerators/mesh_repair_generator/mixed_elements.i)
- (modules/stochastic_tools/test/tests/reporters/AISActiveLearning/ais_al.i)
- (modules/stochastic_tools/test/tests/samplers/dynamic_size/main.i)
- (test/tests/meshgenerators/rename_boundary_generator/name_and_id.i)
- (modules/stochastic_tools/test/tests/surrogates/load_store/train_and_evaluate.i)
- (modules/optimization/test/tests/optimizationreporter/general_opt/main.i)
- (modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter_ci.i)
- (modules/stochastic_tools/test/tests/samplers/AdaptiveImportanceSampler/ais.i)
- (modules/stochastic_tools/test/tests/reporters/parallel_storage/parallel_storage_main.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_distributed.i)
- (modules/stochastic_tools/test/tests/reporters/ActiveLearningGP/main_tao.i)
- (test/tests/outputs/pp_as_reporter/pp_as_reporter.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_distributed.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile.i)
- (modules/reactor/test/tests/positions/hexagonal_grid_positions.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_moment.i)
- (test/tests/positions/multiapp_positions.i)
- (modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_surr.i)
- (modules/stochastic_tools/test/tests/multiapps/transient_with_full_solve/main.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/sobol.i)
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main.i)
- (modules/stochastic_tools/test/tests/reporters/morris/morris.i)
- (modules/stochastic_tools/test/tests/surrogates/cross_validation/train_and_cv.i)
- (test/tests/transfers/multiapp_reporter_transfer/clone_type.i)
- (modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_mixBounds.i)
- (test/tests/meshgenerators/boundary_deletion_generator/boundary_deletion.i)
- (modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_paramBounds.i)
- (modules/stochastic_tools/test/tests/reporters/sobol/sobol.i)
- (modules/stochastic_tools/test/tests/samplers/mcmc/main_des_var.i)
- (test/tests/misc/execute_on/execute_on_test.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_main.i)
- (test/tests/positions/element_group_centroid_positions.i)
- (modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter.i)
- (test/tests/meshgenerators/rename_boundary_generator/rename_boundary.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_locs.i)
- (modules/stochastic_tools/test/tests/samplers/mcmc/main_imh.i)
- (test/tests/times/csv_times.i)
- (test/tests/meshgenerators/rename_block_generator/rename_block.i)
- (test/tests/reporters/iteration_info/iteration_info.i)
- (test/tests/positions/functor_positions.i)
(test/tests/outputs/json/basic/json.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 3
[]
[Reporters]
[test]
type = TestDeclareReporter
[]
[]
[Outputs]
json = true
[]
(test/tests/reporters/iteration_info/gold/limit_out.json)
{
"reporters": {
"iteration_info": {
"type": "IterationInfo",
"values": {
"time": {
"type": "double"
}
}
}
},
"time_steps": [
{
"iteration_info": {
"time": 0.0
},
"time": 0.0,
"time_step": 0
},
{
"iteration_info": {
"time": 1.0
},
"time": 1.0,
"time_step": 1
},
{
"iteration_info": {
"time": 2.0
},
"time": 2.0,
"time_step": 2
},
{
"iteration_info": {
"time": 3.0
},
"time": 3.0,
"time_step": 3
}
]
}
(framework/include/outputs/JsonIO.h)
// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html
#pragma once
#include "MooseError.h"
#include "nlohmann/json.h"
#include "libmesh/libmesh_common.h"
#include <memory>
class MooseApp;
namespace libMesh
{
class Point;
template <typename T>
class DenseVector;
template <typename T>
class DenseMatrix;
}
// Overloads for to_json, which _must_ be overloaded in the namespace
// in which the object is found in order to enable argument-dependent lookup.
// See https://en.cppreference.com/w/cpp/language/adl for more information
void to_json(nlohmann::json & json, const MooseApp & app); // MooseDocs:to_json
namespace libMesh
{
void to_json(nlohmann::json & json, const Point & p);
void to_json(nlohmann::json & json, const DenseVector<Real> & vector);
void to_json(nlohmann::json & json, const DenseMatrix<Real> & matrix);
}
namespace nlohmann
{
template <typename T>
struct adl_serializer<std::unique_ptr<T>>
{
/// Serializer that will output a unique ptr if it exists. We wrap this
/// with is_constructible_v so that we don't specialize types that
/// don't already have a specialization. This is required for some earlier
/// compilers, even though we're not using it at the moment
static void to_json(json & j, const std::unique_ptr<T> & v)
{
if constexpr (std::is_constructible_v<nlohmann::json, T>)
{
if (v)
j = *v;
else
j = nullptr;
}
else
mooseAssert(false, "Should not get to this");
}
};
}
(framework/src/outputs/JsonIO.C)
// This file is part of the MOOSE framework
// https://www.mooseframework.org
//
// All rights reserved, see COPYRIGHT for full restrictions
// https://github.com/idaholab/moose/blob/master/COPYRIGHT
//
// Licensed under LGPL 2.1, please see LICENSE for details
// https://www.gnu.org/licenses/lgpl-2.1.html
#include "JsonIO.h"
#include "MooseApp.h"
#include "MooseRevision.h"
#include "SystemInfo.h"
#include "libmesh/libmesh_config.h"
#include "libmesh/int_range.h"
#include "libmesh/dense_vector.h"
#include "libmesh/dense_matrix.h"
// MooseDocs:to_json_start
void
to_json(nlohmann::json & json, const MooseApp & app)
{
if (!app.getSystemInfo())
return;
json["app_name"] = app.name();
json["current_time"] = app.getSystemInfo()->getTimeStamp();
json["executable"] = app.getSystemInfo()->getExecutable();
json["executable_time"] = app.getSystemInfo()->getExecutableTimeStamp(json["executable"]);
json["moose_version"] = MOOSE_REVISION;
json["libmesh_version"] = LIBMESH_BUILD_VERSION;
#ifdef LIBMESH_DETECTED_PETSC_VERSION_MAJOR
json["petsc_version"] = std::to_string(LIBMESH_DETECTED_PETSC_VERSION_MAJOR) + "." +
std::to_string(LIBMESH_DETECTED_PETSC_VERSION_MINOR) + "." +
std::to_string(LIBMESH_DETECTED_PETSC_VERSION_SUBMINOR);
#endif
#ifdef LIBMESH_DETECTED_SLEPC_VERSION_MAJOR
json["slepc_version"] = std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_MAJOR) + "." +
std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_MINOR) + "." +
std::to_string(LIBMESH_DETECTED_SLEPC_VERSION_SUBMINOR);
#endif
}
// MooseDocs:to_json_end
namespace libMesh
{
void
to_json(nlohmann::json & json, const libMesh::Point & p)
{
json["x"] = p(0);
json["y"] = p(1);
json["z"] = p(2);
}
void
to_json(nlohmann::json & json, const DenseVector<Real> & vector)
{
nlohmann::to_json(json, vector.get_values());
}
void
to_json(nlohmann::json & json, const DenseMatrix<Real> & matrix)
{
std::vector<std::vector<Real>> values(matrix.m(), std::vector<Real>(matrix.n()));
for (const auto i : make_range(matrix.m()))
for (const auto j : make_range(matrix.n()))
values[i][j] = matrix(i, j);
nlohmann::to_json(json, values);
}
}
(modules/stochastic_tools/test/tests/reporters/statistics/statistics.i)
[StochasticTools]
[]
[VectorPostprocessors]
[const]
type = ConstantVectorPostprocessor
value = '1 2 3 4 5'
outputs = none
[]
[]
[Reporters]
[stats]
type = StatisticsReporter
vectorpostprocessors = 'const'
compute = 'min max sum mean stddev norm2'
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/mapping/map_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[D_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 8
distributions = 'S_dist D_dist'
execute_on = initial
min_procs_per_row = 2
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = map_sub.i
sampler = sample
mode = batch-restore
min_procs_per_app = 2
[]
[]
[VariableMappings]
[pod_mapping]
type = PODMapping
solution_storage = parallel_storage
variables = "u v"
num_modes_to_compute = '5 5'
extra_slepc_options = "-svd_monitor_all"
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_u/value BCs/right_v/value'
[]
[solution_transfer]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage
variables = 'u v'
serialize_on_root = false
[]
[]
[Reporters]
[parallel_storage]
type = ParallelSolutionStorage
variables = 'u v'
outputs = none
[]
[reduced_solutions]
type = MappingReporter
sampler = sample
parallel_storage = parallel_storage
mapping = pod_mapping
variables = "u v"
execute_on = timestep_end
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[mapping]
type = MappingOutput
mappings = pod_mapping
[]
file_base = map_training_data
[]
(modules/stochastic_tools/test/tests/reporters/BFActiveLearning/main_adam.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 5
upper_bound = 20
[]
[q_dist]
type = Uniform
lower_bound = 7000
upper_bound = 13000
[]
[Tinf_dist]
type = Uniform
lower_bound = 250
upper_bound = 350
[]
[]
[Samplers]
[mc]
type = ActiveLearningMonteCarloSampler
num_batch = 1
distributions = 'k_dist q_dist Tinf_dist'
flag_sample = 'conditional/flag_sample'
seed = 5
num_samples = 10
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub_lf]
type = SamplerFullSolveMultiApp
sampler = mc
input_files = 'sub_lf.i'
[]
[sub]
type = SamplerFullSolveMultiApp
sampler = mc
input_files = 'sub.i'
mode = batch-reset
should_run_reporter = conditional/need_sample
execute_on = TIMESTEP_END
[]
[]
[Transfers]
[sub]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = mc
parameters = 'Materials/conductivity/prop_values Kernels/source/value BCs/right/value'
check_multiapp_execute_on = false
[]
[sub_lf]
type = SamplerParameterTransfer
to_multi_app = sub_lf
sampler = mc
parameters = 'Materials/conductivity/prop_values Kernels/source/value BCs/right/value'
check_multiapp_execute_on = false
[]
[reporter_transfer_lf]
type = SamplerReporterTransfer
from_reporter = 'avg/value'
stochastic_reporter = 'constant'
from_multi_app = sub_lf
sampler = mc
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'avg/value'
stochastic_reporter = 'conditional'
from_multi_app = sub
sampler = mc
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[conditional]
type = BiFidelityActiveLearningGPDecision
sampler = mc
parallel_type = ROOT
execute_on = 'timestep_begin'
flag_sample = 'flag_sample'
inputs = 'inputs'
gp_mean = 'gp_mean'
gp_std = 'gp_std'
n_train = 8
al_gp = GP_al_trainer
gp_evaluator = GP_eval
learning_function='Ufunction'
learning_function_parameter = 349.345
learning_function_threshold=2.0
outputs_lf = constant/reporter_transfer_lf:avg:value
[]
[]
[Trainers]
[GP_al_trainer]
type = ActiveLearningGaussianProcess
covariance_function = 'covar'
standardize_params = 'true'
standardize_data = 'true'
tune_parameters = 'signal_variance length_factor'
tuning_algorithm = 'adam'
iter_adam = 5000
learning_rate_adam = 0.001
show_optimization_details = true
batch_size = 200
[]
[]
[Surrogates]
[GP_eval]
type = GaussianProcess
trainer = GP_al_trainer
[]
[]
[Covariance]
[covar]
type= SquaredExponentialCovariance
signal_variance = 1.0
noise_variance = 1e-8
length_factor = '1.0 1.0 1.0'
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
file_base = 'single_proc_single_row_ufunction'
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/reporters/nodal_reporter/nodal_stats.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
xmax = 2
ymax = 2
nx = 10
ny = 10
[]
[]
[Variables]
[u]
[]
[]
[Kernels]
[diff]
type = Diffusion
variable = u
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 1
[]
[]
[Reporters]
[nodal_stats]
type = NodalVariableStatistics
coupled_var = u
base_name = diffusion
[]
[]
[Executioner]
type = Steady
solve_type = Newton
petsc_options_iname = '-pc_type'
petsc_options_value = 'lu'
[]
[Outputs]
[stats]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss.i)
[StochasticTools]
[]
[Distributions]
[mu1]
type = Normal
mean = 0.0
standard_deviation = 0.5
[]
[mu2]
type = Normal
mean = 1
standard_deviation = 0.5
[]
[]
[Samplers]
[sample]
type = ParallelSubsetSimulation
distributions = 'mu1 mu2'
num_samplessub = 20
num_subsets = 6
num_parallel_chains = 2
output_reporter = 'constant/reporter_transfer:average:value'
inputs_reporter = 'adaptive_MC/inputs'
seed = 1012
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value BCs/right/value'
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Reporters]
[constant]
type = StochasticReporter
outputs = none
[]
[adaptive_MC]
type = AdaptiveMonteCarloDecision
output_value = constant/reporter_transfer:average:value
inputs = 'inputs'
sampler = sample
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
[out]
type = JSON
[]
[]
(test/tests/positions/reporter_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[input]
type = InputPositions
positions = '0 0 1
1 0 2'
outputs = none
[]
[input_2]
type = InputPositions
positions = '0.1 0.1 1
1.2 0 2.2'
outputs = none
[]
[reporter]
type = ReporterPositions
reporters = 'input/positions_1d input_2/positions_1d'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_batch.i)
[StochasticTools]
[]
[Samplers]
[sample]
type = CartesianProduct
linear_space_items = '0.0 0.1 10'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-restore
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = storage
from_reporter = 'pp/value constant/str'
[]
[runner]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value'
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromInput.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = OptimizationReporter
parameter_names = 'p1 p2 p3'
num_values = '2 4 6'
initial_condition = '1 2; 3 4 5 6; 7 8 9 10 11 12'
upper_bounds = '110; 210; 310'
lower_bounds = '-1; -2; -3'
measurement_points = '0.1 0.2 0.3
0.4 0.5 0.6
0.7 0.8 0.9
1.0 1.1 1.2'
measurement_values = '11 12 13 14'
outputs = out
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 70 80 90 100 110 120'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '-1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -3 -3'
expected_upper_bounds = '110 110 210 210 210 210 310 310 310 310 310 310'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/basic/statistics_vpp.i)
[StochasticTools]
[]
[VectorPostprocessors/const]
type = ConstantVectorPostprocessor
value = '1 2 3 4 5'
[]
[Reporters/stats]
type = StatisticsReporter
vectorpostprocessors = 'const'
compute = 'min max sum mean stddev norm2 ratio stderr'
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(modules/stochastic_tools/test/tests/reporters/basic/statistics_vpp_ci.i)
[StochasticTools]
[]
[VectorPostprocessors/const]
type = ConstantVectorPostprocessor
value = '1 2 3 4 5'
[]
[Reporters/stats]
type = StatisticsReporter
vectorpostprocessors = 'const'
compute = 'min max sum mean stddev norm2 ratio stderr median'
ci_method = percentile
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(modules/stochastic_tools/examples/parameter_study/main.i)
[StochasticTools]
[]
[Distributions]
[gamma]
type = Uniform
lower_bound = 0.5
upper_bound = 2.5
[]
[q_0]
type = Weibull
location = -110
scale = 20
shape = 1
[]
[T_0]
type = Normal
mean = 300
standard_deviation = 45
[]
[s]
type = Normal
mean = 100
standard_deviation = 25
[]
[]
[Samplers]
[hypercube]
type = LatinHypercube
num_rows = 5000
distributions = 'gamma q_0 T_0 s'
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = hypercube
input_files = 'diffusion.i'
mode = batch-restore
[]
[]
[Transfers]
[parameters]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = hypercube
parameters = 'Materials/constant/prop_values Kernels/source/value BCs/right/value BCs/left/value'
[]
[results]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = hypercube
stochastic_reporter = results
from_reporter = 'T_avg/value q_left/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[stats]
type = StatisticsReporter
reporters = 'results/results:T_avg:value results/results:q_left:value'
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[]
[Outputs]
execute_on = 'FINAL'
[out]
type = JSON
[]
[]
(modules/combined/examples/stochastic/lhs_uniform.i)
[StochasticTools]
[]
[Distributions]
[cond_inner]
type = Uniform
lower_bound = 20
upper_bound = 30
[]
[cond_outer]
type = Uniform
lower_bound = 90
upper_bound = 110
[]
[heat_source]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[alpha_inner]
type = Uniform
lower_bound = 1e-6
upper_bound = 3e-6
[]
[alpha_outer]
type = Uniform
lower_bound = 5e-7
upper_bound = 1.5e-6
[]
[ymod_inner]
type = Uniform
lower_bound = 2e5
upper_bound = 2.2e5
[]
[ymod_outer]
type = Uniform
lower_bound = 3e5
upper_bound = 3.2e5
[]
[prat_inner]
type = Uniform
lower_bound = 0.29
upper_bound = 0.31
[]
[prat_outer]
type = Uniform
lower_bound = 0.19
upper_bound = 0.21
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 100000
distributions = 'cond_inner cond_outer heat_source alpha_inner alpha_outer ymod_inner ymod_outer prat_inner prat_outer'
execute_on = INITIAL
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = graphite_ring_thermomechanics.i
sampler = sample
mode = batch-reset
[]
[]
[Transfers]
[sub]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'Materials/cond_inner/prop_values Materials/cond_outer/prop_values
Postprocessors/heat_source/scale_factor
Materials/thermal_strain_inner/thermal_expansion_coeff Materials/thermal_strain_outer/thermal_expansion_coeff
Materials/elasticity_tensor_inner/youngs_modulus Materials/elasticity_tensor_outer/youngs_modulus
Materials/elasticity_tensor_inner/poissons_ratio Materials/elasticity_tensor_outer/poissons_ratio'
check_multiapp_execute_on = false
[]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = storage
from_reporter = 'temp_center_inner/value temp_center_outer/value temp_end_inner/value temp_end_outer/value
dispx_center_inner/value dispx_center_outer/value dispx_end_inner/value dispx_end_outer/value
dispz_inner/value dispz_outer/value'
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[stats]
type = StatisticsReporter
reporters = 'storage/data:temp_center_inner:value storage/data:temp_center_outer:value storage/data:temp_end_inner:value storage/data:temp_end_outer:value
storage/data:dispx_center_inner:value storage/data:dispx_center_outer:value storage/data:dispx_end_inner:value storage/data:dispx_end_outer:value
storage/data:dispz_inner:value storage/data:dispz_outer:value'
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[]
[Outputs]
[out]
type = JSON
[]
execute_on = TIMESTEP_END
[]
(test/tests/meshgenerators/mesh_repair_generator/overlapping_fix_test.i)
[Mesh]
[dir1]
type = ElementGenerator
nodal_positions = '0 0 0
1 0 0
0 1 0'
element_connectivity = '0 1 2'
elem_type = 'TRI3'
[]
[dir2]
type = ElementGenerator
nodal_positions = '1 1 0
1 0 0
0 1 0'
element_connectivity = '0 1 2'
elem_type = 'TRI3'
[]
[combine]
type = CombinerGenerator
inputs = 'dir1 dir2'
[]
[diag]
type = MeshRepairGenerator
input = combine
fix_node_overlap = true
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Reporters]
[mesh]
type = MeshInfo
outputs = json
items = 'num_nodes'
[]
[]
[Outputs]
[json]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/reporters/mesh_meta_data_reporter/mesh_meta_data_reporter.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 1
nx = 1
[]
[metadata]
type = AddMetaDataGenerator
input = gmg
uint_vector_metadata_names = 'foo'
uint_vector_metadata_values = '1 2 3 4'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Reporters/metadata]
type = MeshMetaDataReporter
[]
[Outputs]
[out]
type = JSON
execute_on = initial
execute_system_information_on = none
[]
[]
(test/tests/reporters/constant_reporter/constant_reporter.i)
[Mesh/mesh]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters]
active = constant
[constant]
type = ConstantReporter
integer_names = 'int_1 int_2 int_3'
integer_values = '1 2 -3'
real_names = 'num_1 num_2'
real_values = '4.0 5.0'
string_names = 'str'
string_values = 'six'
dof_id_type_names = 'dofid_1 dofid_2 dofid_3'
dof_id_type_values = '1 2 3'
integer_vector_names = 'int_vec'
integer_vector_values = '7 8'
real_vector_names = 'vec_1 vec_2'
real_vector_values = '8.0 80.0 800.0; 9.0 90.0'
string_vector_names = 'str_vec'
string_vector_values = 'ten eleven twelve thirteen'
dof_id_type_vector_names = 'dofid_vec'
dof_id_type_vector_values = '7 3'
[]
[error]
type = ConstantReporter
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
execute_on = 'timestep_end'
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter_error.i)
[StochasticTools]
[]
[Reporters]
[const]
type = ConstantReporter
string_vector_names = 'const'
string_vector_values = 'a b c d'
[]
[stats]
type = StatisticsReporter
reporters = 'const/const'
compute = 'min max sum mean stddev norm2 ratio stderr'
[]
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(test/tests/controls/libtorch_nn_control/read_control.i)
pi = 3.14159265359
period = 0.25
diff_coeff = 0.5
cp = 1.0
[Functions]
[src_func]
type = ParsedFunction
value = "sin(${pi}/${period}*t)"
[]
[]
[Mesh]
[msh]
type = GeneratedMeshGenerator
dim = 2
nx = 20
xmin = -0.5
xmax = 0.5
ny = 20
ymin = -0.5
ymax = 0.5
[]
[source_domain]
type = ParsedSubdomainMeshGenerator
input = msh
combinatorial_geometry = '(x<0.2 & x>-0.2) & (y<0.2 & y>-0.2)'
block_id = 1
[]
[]
[Variables]
[T]
initial_condition = 1
[]
[]
[Kernels]
[diffusion]
type = CoefDiffusion
variable = T
coef = ${diff_coeff}
[]
[source]
type = BodyForce
variable = T
function = src_func
block = 1
[]
[anti_source]
type = BodyForce
variable = T
value = 0
block = 1
[]
[time_deriv]
type = CoefTimeDerivative
Coefficient = ${cp}
variable = T
[]
[]
[BCs]
[neumann_rest]
type = NeumannBC
variable = T
boundary = 'left right top bottom'
value = 0
[]
[]
[Executioner]
type = Transient
num_steps = 25
dt = 0.1
solve_type = NEWTON
petsc_options_iname = '-pc_type'
petsc_options_value = 'lu'
nl_abs_tol = 1e-12
line_search = 'none'
[]
[Postprocessors]
[T_max]
type = NodalExtremeValue
variable = T
execute_on = 'INITIAL TIMESTEP_END'
[]
[control_value]
type = LibtorchControlValuePostprocessor
control_name = src_control
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Controls]
[src_control]
type = LibtorchNeuralNetControl
parameters = "Kernels/anti_source/value"
responses = 'T_max'
[]
[]
[Reporters]
inactive = 'nn_parameters'
[T_reporter]
type = AccumulateReporter
reporters = 'T_max/value control_value/value'
outputs = csv_out
[]
[nn_parameters]
type = LibtorchArtificialNeuralNetParameters
control_name = src_control
execute_on = FINAL
outputs = json_out
[]
[]
[Outputs]
[csv_out]
type = CSV
execute_on = FINAL
[]
[json_out]
type = JSON
execute_on = FINAL
execute_system_information_on = NONE
[]
[]
(test/tests/utils/shuffle/shuffle.i)
[Mesh/gen]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
solve = 0
[]
[Executioner]
type = Steady
[]
[Reporters/test]
type = TestShuffle
test_type = swap
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/userobjects/inverse_mapping/create_mapping_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[D_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 8
distributions = 'S_dist D_dist'
execute_on = PRE_MULTIAPP_SETUP
min_procs_per_row = 2
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-reset
min_procs_per_app = 2
[]
[]
[Trainers]
[polyreg_v]
type = PolynomialRegressionTrainer
sampler = sample
regression_type = ols
max_degree = 1
response = reduced_solutions/v_pod_mapping
response_type = vector_real
execute_on = FINAL
[]
[polyreg_v_aux]
type = PolynomialRegressionTrainer
sampler = sample
regression_type = ols
max_degree = 1
response = reduced_solutions/v_aux_pod_mapping
response_type = vector_real
execute_on = FINAL
[]
[]
[VariableMappings]
[pod_mapping]
type = PODMapping
solution_storage = parallel_storage
variables = "v v_aux"
num_modes_to_compute = '8 8'
extra_slepc_options = "-svd_monitor_all"
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_v/value BCs/right_v/value'
[]
[solution_transfer]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage
variables = 'v'
serialize_on_root = false
[]
[solution_transfer_aux]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage_aux
variables = 'v_aux'
serialize_on_root = false
[]
[]
[Controls]
[cmd_line]
type = MultiAppSamplerControl
multi_app = worker
sampler = sample
param_names = 'S D'
[]
[]
[Reporters]
[parallel_storage]
type = ParallelSolutionStorage
variables = 'v v_aux'
outputs = none
[]
[reduced_solutions]
type = MappingReporter
sampler = sample
parallel_storage = parallel_storage
mapping = pod_mapping
variables = "v v_aux"
execute_on = timestep_end # To make sure the trainer sees the results on FINAL
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[mapping]
type = MappingOutput
mappings = pod_mapping
execute_on = FINAL
[]
[rom]
type = SurrogateTrainerOutput
trainers = 'polyreg_v polyreg_v_aux'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/reporters/statistics/statistics_distributed.i)
[StochasticTools]
[]
[VectorPostprocessors]
[test]
type = TestDistributedVectorPostprocessor
parallel_type = distributed
outputs = none
[]
[]
[Reporters]
[stats]
type = StatisticsReporter
vectorpostprocessors = 'test'
compute = 'min max sum mean stddev norm2 ratio stderr'
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/optimization/test/tests/optimizationreporter/parameter_mesh_base/paramMeshOptRep.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = ParameterMeshOptimization
parameter_names = 'parameter'
parameter_families = 'LAGRANGE'
parameter_orders = 'FIRST'
parameter_meshes = parameter_mesh_boundsIC_out.e
measurement_points = '0.1 0.2 0.3
0.4 0.5 0.6
0.7 0.8 0.9
1.0 1.1 1.2'
measurement_values = '11 12 13 14'
outputs = outjson
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 0 0 0 0 0 0 0 0 0 0 0 0'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '0 0.5 0.5 0 1 1 0 1 1 0 2 2 0 1.5 1.5 0 3 3'
expected_upper_bounds = '2 2.5 2.5 2 3 3 2 3 3 2 4 4 2 3.5 3.5 2 5 5'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[outjson]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/samplers/mcmc/main_des.i)
[StochasticTools]
[]
[Distributions]
[left]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[right]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'noise_specified/noise_specified'
file_name = 'exp_0_05.csv'
log_likelihood = true
[]
[]
[Samplers]
[sample]
type = AffineInvariantDES
prior_distributions = 'left right'
num_parallel_proposals = 5
file_name = 'confg.csv'
execute_on = PRE_MULTIAPP_SETUP
seed = 2547
initial_values = '0.1 0.1'
previous_state = 'mcmc_reporter/inputs'
previous_state_var = 'mcmc_reporter/variance'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
sampler = sample
param_names = 'left_bc right_bc mesh1'
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[noise_specified]
type = ConstantReporter
real_names = 'noise_specified'
real_values = '0.05'
[]
[mcmc_reporter]
type = AffineInvariantDifferentialDecision
output_value = constant/reporter_transfer:average:value
sampler = sample
likelihoods = 'gaussian'
[]
[]
[Executioner]
type = Transient
num_steps = 5
[]
[Outputs]
file_base = 'des_5prop'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/test/tests/likelihoods/gaussian_derived/main.i)
[StochasticTools]
[]
[Distributions]
[mu1]
type = Normal
mean = 0.0
standard_deviation = 0.5
[]
[]
[Samplers]
[sample]
type = MonteCarlo
distributions = 'mu1 mu1'
num_rows = 3
seed = 2547
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value BCs/right/value'
# to_control = 'stochastic'
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[noise_specified]
type = ConstantReporter
real_names = 'noise_specified'
real_values = '0.2'
[]
[likelihoodtest]
type = TestLikelihood
likelihoods = 'gaussian'
model_pred = constant/reporter_transfer:average:value
sampler = sample
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'noise_specified/noise_specified'
file_name = 'exp1.csv'
log_likelihood=true
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
file_base ='loglikelihood_scalar'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/transfers/multiapp_reporter_transfer/main.i)
[Mesh/generate]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[VectorPostprocessors]
[from_main_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b c'
value = '1 1 1; 2 2 2; 3 3 3'
execute_on = initial
#outputs = none
[]
[to_main_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b c'
value = '4 4 4; 5 5 5; 6 6 6'
#outputs = none
[]
[]
[Reporters]
[from_main_rep]
type = ConstantReporter
integer_names = int
integer_values = 1
real_names = num
real_values = 2.0
real_vector_names = vec
real_vector_values = '3 4'
string_names = str
string_values = 'five'
[]
[to_main_rep]
type = ConstantReporter
integer_names = int
integer_values = 0
real_names = num
real_values = 0.0
real_vector_names = vec
real_vector_values = '0'
string_names = str
string_values = 'foo'
[]
[]
[MultiApps/sub]
type = TransientMultiApp
input_files = 'sub0.i sub1.i'
positions = '0 0 0 0 0 0'
[]
[Transfers]
# VPP transfers
[vpp_to_vpp]
type = MultiAppReporterTransfer
to_reporters = 'to_sub_vpp/a to_sub_vpp/b'
from_reporters = 'from_main_vpp/a from_main_vpp/b'
to_multi_app = sub
[]
[vpp_from_vpp]
type = MultiAppReporterTransfer
to_reporters = 'to_main_vpp/a to_main_vpp/b'
from_reporters = 'from_sub_vpp/a from_sub_vpp/b'
from_multi_app = sub
subapp_index = 0
[]
# Vector-VPP transfers
[vector_to_vpp]
type = MultiAppReporterTransfer
to_reporters = 'to_sub_vpp/a'
from_reporters = 'from_main_rep/vec'
to_multi_app = sub
subapp_index = 0
[]
[vector_from_vpp]
type = MultiAppReporterTransfer
to_reporters = 'to_main_rep/vec'
from_reporters = 'from_sub_vpp/a'
from_multi_app = sub
subapp_index = 0
[]
# Real-PP transfers
[real_to_pp]
type = MultiAppReporterTransfer
to_reporters = 'to_sub_pp/value'
from_reporters = 'from_main_rep/num'
to_multi_app = sub
subapp_index = 0
[]
[real_from_pp]
type = MultiAppReporterTransfer
to_reporters = 'to_main_rep/num'
from_reporters = 'from_sub_pp/value'
from_multi_app = sub
subapp_index = 0
[]
# Int-Int transfers
[int_to_int]
type = MultiAppReporterTransfer
to_reporters = 'to_sub_rep/int'
from_reporters = 'from_main_rep/int'
to_multi_app = sub
subapp_index = 0
[]
[int_from_int]
type = MultiAppReporterTransfer
to_reporters = 'to_main_rep/int'
from_reporters = 'from_sub_rep/int'
from_multi_app = sub
subapp_index = 0
[]
# String-String transfers
[string_to_string]
type = MultiAppReporterTransfer
to_reporters = 'to_sub_rep/str'
from_reporters = 'from_main_rep/str'
to_multi_app = sub
subapp_index = 0
[]
[string_from_string]
type = MultiAppReporterTransfer
to_reporters = 'to_main_rep/str'
from_reporters = 'from_sub_rep/str'
from_multi_app = sub
subapp_index = 0
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
execute_on = timestep_end
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_main.i)
[StochasticTools]
[]
[Distributions]
[n0]
type = Normal
mean = 0
standard_deviation = 1
[]
[n1]
type = Normal
mean = 1
standard_deviation = 1
[]
[n2]
type = Normal
mean = 2
standard_deviation = 0.5
[]
[n3]
type = Normal
mean = 3
standard_deviation = 0.33333333333
[]
[n4]
type = Normal
mean = 4
standard_deviation = 0.25
[]
[]
[Samplers/sample]
type = MonteCarlo
distributions = 'n0 n1 n2 n3 n4'
num_rows = 100
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = sample
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
mode = batch-reset
[]
[Controls/param]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Reporters/const/real_vector_values[0,1,2,3,4]'
[]
[Transfers/data]
type = SamplerReporterTransfer
from_multi_app = sub
from_reporter = 'const/num_vec'
stochastic_reporter = storage
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = storage/data:const:num_vec
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 10000
ci_seed = 1945
execute_on = FINAL
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(test/tests/transfers/multiapp_reporter_transfer/sub0.i)
[Mesh/generate]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Postprocessors]
[to_sub_pp]
type = Receiver
[]
[from_sub_pp]
type = Receiver
default = 3.1415926
[]
[]
[VectorPostprocessors]
[to_sub_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b'
value = '10 10 10 ; 20 20 20'
[]
[from_sub_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b'
value = '30 30 30; 40 40 40'
[]
[]
[Reporters]
[to_sub_rep]
type = ConstantReporter
integer_names = int
integer_values = 0
string_names = str
string_values = 'foo'
[]
[from_sub_rep]
type = ConstantReporter
integer_names = int
integer_values = 10
string_names = str
string_values = 'twenty'
[]
[]
[Executioner]
type = Transient
num_steps = 0
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
postprocessors_as_reporters = true
[]
execute_on = timestep_end
[]
(test/tests/meshgenerators/parsed_element_deletion_generator/cut_in_half.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
nx = 2
ny = 2
[]
[delete]
type = ParsedElementDeletionGenerator
input = gmg
expression = 'y < 0.49'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Reporters]
[mesh]
type = MeshInfo
items = 'num_nodes num_elements'
outputs = json
[]
[]
[Outputs]
[json]
type = JSON
execute_system_information_on = NONE
execute_on = 'TIMESTEP_END'
[]
[]
(modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_small.i)
[StochasticTools]
[]
[Samplers]
[sample]
type = CartesianProduct
execute_on = PRE_MULTIAPP_SETUP
linear_space_items = '0 1 3'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
ignore_solve_not_converge = true
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = storage
from_reporter = 'pp/value'
[]
[]
[Controls]
[runner]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'BCs/left/value'
sampler = sample
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(test/tests/positions/element_centroid_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
ix = 3
iy = 4
iz = 5
dx = 1
dy = 2
dz = 1.5
dim = 3
[]
[new_block]
type = ParsedSubdomainMeshGenerator
input = cmg
combinatorial_geometry = 'x>0.6&z<1.1'
block_name = 1
block_id = 1
[]
[]
[Positions]
[all_mesh]
type = ElementCentroidPositions
# For testing reproducibility
auto_sort = true
[]
[block_1]
type = ElementCentroidPositions
block = 1
# For testing reproducibility
auto_sort = true
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Postprocessors]
[num_pos]
type = NumPositions
positions = all_mesh
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/transfers/serialized_solution_transfer/sst_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[D_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 4
distributions = 'S_dist D_dist'
execute_on = initial
min_procs_per_row = 2
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-restore
min_procs_per_app = 2
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_u/value BCs/right_v/value'
[]
[solution_transfer]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage
variables = 'u v'
serialize_on_root = true
[]
[]
[Reporters]
[parallel_storage]
type = ParallelSolutionStorage
variables = 'u v'
outputs = out
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
file_base = "serialization"
[]
(test/tests/times/time_interval_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[times]
type = TimeIntervalTimes
time_interval = 2.0
always_include_end_time = true
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
start_time = 5.0
end_time = 10.0
num_steps = 2
[]
[Outputs]
file_base = 'time_interval_with_end'
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/reporters/perf_graph_reporter/perf_graph_reporter.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
nx = 1
ny = 1
[]
[]
[Variables/u]
[]
[Kernels/diff]
type = Diffusion
variable = u
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 1
[]
[]
[Executioner]
type = Steady
solve_type = 'PJFNK'
petsc_options_iname = '-pc_type -pc_hypre_type'
petsc_options_value = 'hypre boomeramg'
[]
[Reporters/perf_graph]
type = PerfGraphReporter
execute_on = FINAL
[]
[Outputs/json]
type = JSON
execute_on = 'INITIAL FINAL'
[]
(test/tests/reporters/iteration_info/iteration_info_steady.i)
[Mesh]
[generate]
type = GeneratedMeshGenerator
dim = 1
nx = 10
[]
[]
[Variables/u][]
[Kernels]
[diff]
type = ADDiffusion
variable = u
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 10
[]
[]
[Executioner]
type = Steady
solve_type = NEWTON
petsc_options_iname = '-pc_type -pc_hypre_type'
petsc_options_value = 'hypre boomeramg'
[]
[Reporters/iteration_info]
type = IterationInfo
[]
[Outputs]
[out]
type = JSON
[]
[]
(test/tests/outputs/hide_via_reporters_block/reporter.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Problem]
solve = false
kernel_coverage_check = false
[]
[Reporters]
[day]
type = ConstantReporter
integer_names = day
integer_values = 24
[]
[month]
type = ConstantReporter
integer_names = month
integer_values = 6
outputs = none
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_locs.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Normal
mean = 5
standard_deviation = 0.5
[]
[S_dist]
type = Normal
mean = 8
standard_deviation = 0.7
[]
[]
[Samplers]
[grid]
type = CartesianProduct
linear_space_items = '2.5 0.5 10 3 1 10'
[]
[quadrature]
type = Quadrature
distributions = 'D_dist S_dist'
execute_on = INITIAL
order = 5
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = quadrature
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = quadrature
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[local_sense]
type = PolynomialChaosReporter
pc_name = poly_chaos
local_sensitivity_sampler = grid
local_sensitivity_points = '3.14159 3.14159 2.7182 3.14159 3.14159 2.7182 2.7182 2.7182'
execute_on = final
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 5
distributions = 'D_dist S_dist'
sampler = quadrature
response = storage/data:avg:value
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/times/input_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[input]
type = InputTimes
times = '0.2 0.4 0.9'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
# Test recover
num_steps = 2
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/multiapps/dynamic_sub_app_number/main.i)
[StochasticTools]
auto_create_executioner = false
[]
[Distributions]
[uniform]
type = Uniform
lower_bound = 1
upper_bound = 2
[]
[]
[Samplers]
[mc]
type = TestDynamicNumberOfSubAppsSampler
num_rows = 5
distributions = 'uniform'
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Executioner]
type = Transient
num_steps = 3
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = mc
input_files = 'sub.i'
execute_on = 'TIMESTEP_BEGIN'
[]
[]
[Transfers]
[runner]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = mc
parameters = 'BCs/right/value'
[]
[data]
type = SamplerPostprocessorTransfer
from_multi_app = runner
sampler = mc
to_vector_postprocessor = storage
from_postprocessor = center
[]
[]
[VectorPostprocessors]
[storage]
type = StochasticResults
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
[]
(modules/stochastic_tools/test/tests/variablemappings/pod_mapping/pod_mapping_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[D_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 4
distributions = 'S_dist D_dist'
execute_on = initial
min_procs_per_row = 2
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-restore
min_procs_per_app = 2
[]
[]
[VariableMappings]
[pod_mapping]
type = PODMapping
solution_storage = parallel_storage
variables = "v"
num_modes_to_compute = '2'
extra_slepc_options = "-svd_monitor_all"
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_v/value BCs/right_v/value'
[]
[solution_transfer]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage
variables = 'v'
serialize_on_root = true
[]
[]
[Reporters]
[parallel_storage]
type = ParallelSolutionStorage
variables = 'v'
outputs = none
[]
[svd_output]
type = SingularTripletReporter
variables = 'v'
pod_mapping = pod_mapping
execute_on = FINAL
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = NONE
[]
[]
(test/tests/times/reporter_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[input]
type = InputTimes
times = '0.2 0.4 0.9'
outputs = none
[]
[input_2]
type = InputTimes
times = '0.2 0.5 0.6'
outputs = none
[]
[reporter]
type = ReporterTimes
reporters = 'input/times input_2/times'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(test/tests/reporters/declare_initial_setup/declare_initial_setup.i)
[Mesh]
[generate]
type = GeneratedMeshGenerator
dim = 1
[]
[]
[Variables/u]
[]
[Executioner]
type = Steady
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters]
active = initialSetup
[initialSetup]
type = TestDeclareInitialSetupReporter
value = 1980
[]
[info]
type = MeshInfo
items = num_elements
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/positions/distributed_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[input]
type = InputPositions
positions = '0 0 0
1 0 0
0 1 0
1 1 0'
outputs = none
[]
[input_2]
type = InputPositions
positions = '10 0 0
0 10 0'
outputs = none
[]
[distributed]
type = DistributedPositions
positions = 'input_2 input'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/mapping/load_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[D_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 8
distributions = 'S_dist D_dist'
execute_on = initial
min_procs_per_row = 2
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = map_sub.i
sampler = sample
mode = batch-restore
min_procs_per_app = 2
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_u/value BCs/right_v/value'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = worker
stochastic_reporter = results
from_reporter = 'pod_coeffs/u_pod pod_coeffs/v_pod'
sampler = sample
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Outputs]
[json]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
file_base = map_variable
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Normal
mean = 5
standard_deviation = 2
[]
[q_dist]
type = Normal
mean = 10000
standard_deviation = 500
[]
[L_dist]
type = Normal
mean = 0.03
standard_deviation = 0.01
[]
[Tinf_dist]
type = Normal
mean = 300
standard_deviation = 10
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 100000
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = initial
[]
[]
[Surrogates]
[poly_chaos_avg]
type = PolynomialChaos
filename = 'poly_chaos_training_poly_chaos_avg.rd'
[]
[poly_chaos_max]
type = PolynomialChaos
filename = 'poly_chaos_training_poly_chaos_max.rd'
[]
[]
[Reporters]
[samp]
type = EvaluateSurrogate
model = 'poly_chaos_avg poly_chaos_max'
sampler = sample
parallel_type = ROOT
[]
[stats]
type = PolynomialChaosReporter
pc_name = 'poly_chaos_avg poly_chaos_max'
statistics = 'mean stddev'
local_sensitivity_points = '5 10000 0.03 300; 5 10000 0.03 300'
include_sobol = true
[]
[]
[Outputs]
[out]
type = JSON
execute_on = final
[]
[]
(modules/stochastic_tools/test/tests/reporters/morris/morris_main.i)
[StochasticTools]
[]
[Distributions/uniform]
type = Uniform
lower_bound = 0
upper_bound = 1
[]
[Samplers/morris]
type = MorrisSampler
distributions = 'uniform uniform uniform uniform uniform uniform'
trajectories = 10
levels = 4
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = morris
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
mode = batch-reset
[]
[Controls/param]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'x0 x1 x2 x3 x4 x5'
[]
[Transfers/data]
type = SamplerReporterTransfer
from_multi_app = sub
from_reporter = 'const/gf const/gfa const/gf_vec'
stochastic_reporter = storage
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = NONE
[]
[morris]
type = MorrisReporter
reporters = 'storage/data:const:gf storage/data:const:gfa storage/data:const:gf_vec'
ci_levels = '0.1 0.9'
ci_replicates = 1000
execute_on = FINAL
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/transfers/multiapp_reporter_transfer/sub1.i)
[Mesh/generate]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[VectorPostprocessors]
[to_sub_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b'
value = '11 11 11 ; 21 21 21'
[]
[from_sub_vpp]
type = ConstantVectorPostprocessor
vector_names = 'a b'
value = '31 31 31; 41 41 41'
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
[]
execute_on = timestep_end
[]
(test/tests/reporters/element_reporter/elem_stats.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
xmax = 2
ymax = 2
nx = 10
ny = 10
[]
[]
[Variables]
[u]
[]
[]
[Kernels]
[diff]
type = Diffusion
variable = u
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 1
[]
[]
[Reporters]
[elem_stats]
type = ElementVariableStatistics
coupled_var = u
base_name = diffusion
[]
[]
[Executioner]
type = Steady
solve_type = Newton
petsc_options_iname = '-pc_type'
petsc_options_value = 'lu'
[]
[Outputs]
[stats]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[L_dist]
type = Uniform
lower_bound = 0.01
upper_bound = 0.05
[]
[Tinf_dist]
type = Uniform
lower_bound = 290
upper_bound = 310
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 100000
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = initial
[]
[]
[Surrogates]
[poly_chaos_avg]
type = PolynomialChaos
filename = 'poly_chaos_training_poly_chaos_avg.rd'
[]
[poly_chaos_max]
type = PolynomialChaos
filename = 'poly_chaos_training_poly_chaos_max.rd'
[]
[]
[Reporters]
[samp]
type = EvaluateSurrogate
model = 'poly_chaos_avg poly_chaos_max'
sampler = sample
parallel_type = ROOT
[]
[stats]
type = PolynomialChaosReporter
pc_name = 'poly_chaos_avg poly_chaos_max'
statistics = 'mean stddev'
local_sensitivity_points = '5 10000 0.03 300; 5 10000 0.03 300'
include_sobol = true
[]
[]
[Outputs]
[out]
type = JSON
execute_on = final
[]
[]
(modules/stochastic_tools/test/tests/samplers/mcmc/main_ss.i)
[StochasticTools]
[]
[Distributions]
[left]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[right]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'noise_specified/noise_specified'
file_name = 'exp_0_05.csv'
log_likelihood = true
[]
[]
[Samplers]
[sample]
type = AffineInvariantStretchSampler
prior_distributions = 'left right'
num_parallel_proposals = 5
file_name = 'confg.csv'
execute_on = PRE_MULTIAPP_SETUP
seed = 2547
initial_values = '0.1 0.1'
previous_state = 'mcmc_reporter/inputs'
previous_state_var = 'mcmc_reporter/variance'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
sampler = sample
param_names = 'left_bc right_bc mesh1'
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[noise_specified]
type = ConstantReporter
real_names = 'noise_specified'
real_values = '0.05'
[]
[mcmc_reporter]
type = AffineInvariantStretchDecision
output_value = constant/reporter_transfer:average:value
sampler = sample
likelihoods = 'gaussian'
[]
[]
[Executioner]
type = Transient
num_steps = 5
[]
[Outputs]
file_base = 'ss_5prop'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/positions/file_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[file]
type = FilePositions
files = '../multiapps/positions_from_file/positions.txt'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/examples/parameter_study/main_vector.i)
[StochasticTools]
[]
[Distributions]
[gamma]
type = Uniform
lower_bound = 0.5
upper_bound = 2.5
[]
[q_0]
type = Weibull
location = -110
scale = 20
shape = 1
[]
[T_0]
type = Normal
mean = 300
standard_deviation = 45
[]
[s]
type = Normal
mean = 100
standard_deviation = 25
[]
[]
[Samplers]
[hypercube]
type = LatinHypercube
num_rows = 5000
distributions = 'gamma q_0 T_0 s'
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = hypercube
input_files = 'diffusion_vector.i'
mode = batch-restore
[]
[]
[Transfers]
[parameters]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = hypercube
parameters = 'Materials/constant/prop_values Kernels/source/value BCs/right/value BCs/left/value'
[]
[results]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = hypercube
stochastic_reporter = results
from_reporter = 'acc/T_avg:value acc/q_left:value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = 'results/results:acc:T_avg:value results/results:acc:q_left:value'
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[]
[Outputs]
execute_on = 'FINAL'
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/samplers/mcmc/main_base.i)
[StochasticTools]
[]
[Distributions]
[left]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[right]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'noise_specified/noise_specified'
file_name = 'exp_0_05.csv'
log_likelihood = true
[]
[]
[Samplers]
[sample]
type = PMCMCBase
prior_distributions = 'left right'
num_parallel_proposals = 2
initial_values = '0.1 0.1'
file_name = 'confg.csv'
execute_on = PRE_MULTIAPP_SETUP
seed = 2547
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
sampler = sample
param_names = 'left_bc right_bc mesh1'
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[noise_specified]
type = ConstantReporter
real_names = 'noise_specified'
real_values = '0.05'
[]
[mcmc_reporter]
type = PMCMCDecision
output_value = constant/reporter_transfer:average:value
sampler = sample
likelihoods = 'gaussian'
[]
[]
[Executioner]
type = Transient
num_steps = 5
[]
[Outputs]
file_base = 'mcmc_base'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/reporters/restartable_data_reporter/restartable_data_reporter.i)
[Mesh/gmg]
type = GeneratedMeshGenerator
dim = 1
nx = 1
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 2
[]
[Reporters/data]
type = RestartableDataReporter
include = 'FEProblemBase/*/time*'
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/transfers/multiapp_reporter_transfer/clone.i)
[Mesh/generate]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters]
[receiver]
type = ConstantReporter
[]
[]
[MultiApps]
[multi_vector]
type = TransientMultiApp
input_files = 'sub0.i sub0.i sub1.i sub1.i'
positions = '0 0 0
0 0 0
0 0 0
0 0 0'
cli_args = 'Outputs/active="" Outputs/active="" Outputs/active="" Outputs/active=""'
[]
[multi_reporter]
type = TransientMultiApp
input_files = 'sub0.i sub0.i sub0.i sub0.i'
positions = '0 0 0
0 0 0
0 0 0
0 0 0'
cli_args = 'Postprocessors/from_sub_pp/default=3.1415926;Reporters/from_sub_rep/integer_values=10;Reporters/from_sub_rep/string_values=ten;Outputs/active=""
Postprocessors/from_sub_pp/default=1.5707963;Reporters/from_sub_rep/integer_values=11;Reporters/from_sub_rep/string_values=twenty;Outputs/active=""
Postprocessors/from_sub_pp/default=1.0471975;Reporters/from_sub_rep/integer_values=12;Reporters/from_sub_rep/string_values=thirty;Outputs/active=""
Postprocessors/from_sub_pp/default=0.7853981;Reporters/from_sub_rep/integer_values=13;Reporters/from_sub_rep/string_values=forty;Outputs/active=""'
[]
[single_app]
type = TransientMultiApp
input_files = 'sub0.i'
cli_args = 'Outputs/active=""'
[]
[]
[Transfers]
[multi_vpp]
type = MultiAppCloneReporterTransfer
from_reporters = 'from_sub_vpp/a from_sub_vpp/b'
to_reporter = receiver
from_multi_app = multi_vector
[]
[multi_rep]
type = MultiAppCloneReporterTransfer
from_reporters = 'from_sub_pp/value from_sub_rep/int from_sub_rep/str'
to_reporter = receiver
from_multi_app = multi_reporter
[]
[single]
type = MultiAppCloneReporterTransfer
from_reporters = 'from_sub_pp/value from_sub_rep/int from_sub_rep/str'
to_reporter = receiver
from_multi_app = single_app
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
execute_on = timestep_end
[]
(modules/stochastic_tools/test/tests/surrogates/polynomial_regression/poly_reg_vec.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Normal
mean = 5
standard_deviation = 2
[]
[L_dist]
type = Normal
mean = 0.03
standard_deviation = 0.01
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 10
distributions = 'k_dist L_dist'
execute_on = PRE_MULTIAPP_SETUP
min_procs_per_row = 2
[]
[]
[GlobalParams]
sampler = sample
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub_vector.i
mode = batch-reset
execute_on = initial
min_procs_per_app = 2
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Materials/conductivity/prop_values L'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
stochastic_reporter = results
from_reporter = 'T_vec/T T_vec/x'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[eval]
type = EvaluateSurrogate
model = pr_surrogate
response_type = vector_real
parallel_type = ROOT
execute_on = timestep_end
[]
[]
[Trainers]
[pr]
type = PolynomialRegressionTrainer
regression_type = ols
max_degree = 2
response = results/data:T_vec:T
response_type = vector_real
execute_on = initial
[]
[]
[Surrogates]
[pr_surrogate]
type = PolynomialRegressionSurrogate
trainer = pr
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_surr.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Normal
mean = 5
standard_deviation = 2
[]
[q_dist]
type = Normal
mean = 10000
standard_deviation = 500
[]
[L_dist]
type = Normal
mean = 0.03
standard_deviation = 0.01
[]
[Tinf_dist]
type = Normal
mean = 300
standard_deviation = 10
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 100000
distributions = 'k_dist q_dist L_dist Tinf_dist'
[]
[]
[Surrogates]
[pc_max]
type = PolynomialChaos
filename = 'normal_train_pc_out_pc_max.rd'
[]
[pr_max]
type = PolynomialRegressionSurrogate
filename = 'normal_train_pr_out_pr_max.rd'
[]
[]
[Reporters]
[pc_max_res]
type = EvaluateSurrogate
model = pc_max
sampler = sample
parallel_type = ROOT
[]
[pr_max_res]
type = EvaluateSurrogate
model = pr_max
sampler = sample
parallel_type = ROOT
[]
[pr_max_stats]
type = StatisticsReporter
reporters = 'pr_max_res/pr_max'
compute = 'mean stddev'
[]
[pc_max_stats]
type = PolynomialChaosReporter
pc_name = 'pc_max'
statistics = 'mean stddev'
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_surr.i)
[StochasticTools]
[]
[Distributions]
[C_dist]
type = Uniform
lower_bound = 0.01
upper_bound = 0.02
[]
[f_dist]
type = Uniform
lower_bound = 15
upper_bound = 25
[]
[init_dist]
type = Uniform
lower_bound = 270
upper_bound = 330
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 100000
distributions = 'C_dist f_dist init_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[Surrogates]
[pc_min]
type = PolynomialChaos
filename = 'trans_diff_trainer_out_pc_min.rd'
[]
[pc_max]
type = PolynomialChaos
filename = 'trans_diff_trainer_out_pc_max.rd'
[]
[pr_min]
type = PolynomialRegressionSurrogate
filename = 'trans_diff_trainer_out_pr_min.rd'
[]
[pr_max]
type = PolynomialRegressionSurrogate
filename = 'trans_diff_trainer_out_pr_max.rd'
[]
[np_min]
type = NearestPointSurrogate
filename = 'trans_diff_trainer_out_np_min.rd'
[]
[np_max]
type = NearestPointSurrogate
filename = 'trans_diff_trainer_out_np_max.rd'
[]
[]
# Computing statistics
[Reporters]
[eval_surr]
type = EvaluateSurrogate
model = 'pc_max pc_min pr_max pr_min np_max np_min'
sampler = sample
parallel_type = ROOT
[]
[eval_surr_stats]
type = StatisticsReporter
reporters = 'eval_surr/pc_max eval_surr/pc_min eval_surr/pr_max eval_surr/pr_min eval_surr/np_max eval_surr/np_min'
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca.i)
[StochasticTools]
[]
[VectorPostprocessors]
# Table 14.1 in Efron and Tibshirani, 1993
[A]
type = ConstantVectorPostprocessor
value = '48 36 20 29 42 42 20 42 22 41 45 14 6 0 33 28 34 4 32 24 47 41 24 26 30 41'
outputs = none
[]
[B]
type = ConstantVectorPostprocessor
value = '42 33 16 39 38 36 15 33 20 43 34 22 7 15 34 29 41 13 38 25 27 41 28 14 28 40'
outputs = none
[]
[]
[Reporters]
# Reproduce Table 13.1 in Efron and Tibshirani, 1993
[stats]
type = StatisticsReporter
vectorpostprocessors = 'A B'
compute = 'mean'
ci_method = 'bca'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 1000
ci_seed = 1980
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(test/tests/positions/input_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[input]
type = InputPositions
positions = '0.1 0 0
0.2 0 0.1'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
# Test recover
num_steps = 2
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/examples/parameter_study/main_time.i)
[StochasticTools]
[]
[Distributions]
[gamma]
type = Uniform
lower_bound = 0.5
upper_bound = 2.5
[]
[q_0]
type = Weibull
location = -110
scale = 20
shape = 1
[]
[T_0]
type = Normal
mean = 300
standard_deviation = 45
[]
[s]
type = Normal
mean = 100
standard_deviation = 25
[]
[]
[Samplers]
[hypercube]
type = LatinHypercube
num_rows = 5000
distributions = 'gamma q_0 T_0 s'
[]
[]
[MultiApps]
[runner]
type = SamplerTransientMultiApp
sampler = hypercube
input_files = 'diffusion_time.i'
mode = batch-restore
[]
[]
[Transfers]
[parameters]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = hypercube
parameters = 'Materials/constant/prop_values Kernels/source/value BCs/right/value BCs/left/value'
[]
[results]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = hypercube
stochastic_reporter = results
from_reporter = 'T_avg/value q_left/value T_vec/T'
[]
[x_transfer]
type = MultiAppReporterTransfer
from_multi_app = runner
subapp_index = 0
from_reporters = T_vec/x
to_reporters = const/x
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = 'results/results:T_avg:value results/results:q_left:value results/results:T_vec:T'
compute = 'mean stddev'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[const]
type = ConstantReporter
real_vector_names = 'x'
real_vector_values = '0'
[]
[]
[Executioner]
type = Transient
num_steps = 4
dt = 0.25
[]
[Outputs]
execute_on = timestep_end
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss_error1.i)
[StochasticTools]
[]
[Distributions]
[mu1]
type = Normal
mean = 0.0
standard_deviation = 0.5
[]
[mu2]
type = Normal
mean = 1
standard_deviation = 0.5
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 1
distributions = 'mu1 mu2'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value BCs/right/value'
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Reporters]
[constant]
type = StochasticReporter
outputs = none
[]
[adaptive_MC]
type = AdaptiveMonteCarloDecision
output_value = constant/reporter_transfer:average:value
inputs = 'inputs'
sampler = sample
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
[]
[]
(test/tests/outputs/vpp_as_reporter/vpp_as_reporter.i)
[Mesh/gen]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[VectorPostprocessors/data]
type = ConstantVectorPostprocessor
vector_names = 'vector'
value = '1949 1954 1977 1980'
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
[]
(modules/stochastic_tools/test/tests/multiapps/conditional_run/conditional_main.i)
[StochasticTools]
[]
[Samplers]
[cart]
type = CartesianProduct
linear_space_items = '1 1 3
1 1 3'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = cart
input_files = 'sub.i'
mode = batch-reset
should_run_reporter = conditional/need_sample
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = cart
from_reporter = 'average/value'
stochastic_reporter = conditional
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = runner
sampler = cart
param_names = 'BCs/left/value BCs/right/value'
[]
[]
[Reporters]
[conditional]
type = ConditionalSampleReporter
sampler = cart
default_value = 999
function = 'val1 * val2 >= t'
sampler_vars = 'val1 val2'
sampler_var_indices = '0 1'
parallel_type = ROOT
execute_on = 'initial timestep_begin'
[]
[]
[Executioner]
type = Transient
num_steps = 4
[]
[Outputs]
execute_on = timestep_end
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/sobol/sobol_main.i)
[StochasticTools]
[]
[Distributions/uniform]
type = Uniform
lower_bound = 0
upper_bound = 1
[]
[Samplers]
[sample]
type = MonteCarlo
distributions = 'uniform uniform uniform uniform uniform uniform'
num_rows = 10
seed = 0
execute_on = PRE_MULTIAPP_SETUP
[]
[resample]
type = MonteCarlo
distributions = 'uniform uniform uniform uniform uniform uniform'
num_rows = 10
seed = 1
execute_on = PRE_MULTIAPP_SETUP
[]
[sobol]
type = Sobol
sampler_a = sample
sampler_b = resample
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[GlobalParams]
sampler = sobol
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
mode = batch-reset
[]
[Controls/param]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'x0 x1 x2 x3 x4 x5'
[]
[Transfers/data]
type = SamplerReporterTransfer
from_multi_app = sub
from_reporter = 'const/gf const/gfa const/gf_vec'
stochastic_reporter = storage
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = NONE
[]
[sobol]
type = SobolReporter
reporters = 'storage/data:const:gf storage/data:const:gfa storage/data:const:gf_vec'
ci_levels = '0.1 0.9'
ci_replicates = 1000
execute_on = FINAL
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(test/tests/outputs/json/one_file_per_timestep/json.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 3
[]
[Reporters]
[test]
type = ConstantReporter
integer_names = 'year'
integer_values = '1980'
execute_on = INITIAL
[]
[]
[Outputs]
[out]
type = JSON
one_file_per_timestep = true
[]
[]
(modules/stochastic_tools/test/tests/transfers/libtorch_nn_transfer/libtorch_drl_control_sub.i)
[Mesh]
[mesh]
type = GeneratedMeshGenerator
dim = 1
xmin = 0.0
xmax = 7.0
nx = 3
[]
[]
[Variables]
[temp]
initial_condition = 300
[]
[]
[Kernels]
[time]
type = CoefTimeDerivative
variable = temp
Coefficient = '${fparse 1.00630182*1.225}'
[]
[heat_conduc]
type = MatDiffusion
variable = temp
diffusivity = 'k'
[]
[]
[BCs]
[left_flux]
type = NeumannBC
value = 0.0
boundary = 'left'
variable = temp
[]
[dirichlet]
type = FunctionDirichletBC
function = temp_env
variable = temp
boundary = 'right'
[]
[]
[Functions]
[temp_env]
type = ParsedFunction
value = '15.0*sin(t/86400.0 *pi) + 273.0'
[]
[design_function]
type = ParsedFunction
value = '297'
[]
[reward_function]
type = ScaledAbsDifferenceDRLRewardFunction
design_function = design_function
observed_value = center_temp_tend
c1 = 1
c2 = 10
[]
[]
[Materials]
[constant]
type = GenericConstantMaterial
prop_names = 'k'
prop_values = 26.53832364
[]
[]
[Postprocessors]
[center_temp]
type = PointValue
variable = temp
point = '3.5 0.0 0.0'
execute_on = 'INITIAL TIMESTEP_BEGIN'
[]
[center_temp_tend]
type = PointValue
variable = temp
point = '3.5 0.0 0.0'
execute_on = 'INITIAL TIMESTEP_END'
[]
[env_temp]
type = FunctionValuePostprocessor
function = temp_env
execute_on = 'INITIAL TIMESTEP_BEGIN'
[]
[reward]
type = FunctionValuePostprocessor
function = reward_function
execute_on = 'INITIAL TIMESTEP_END'
indirect_dependencies = 'center_temp_tend env_temp'
[]
[left_flux]
type = LibtorchControlValuePostprocessor
control_name = src_control
execute_on = 'INITIAL TIMESTEP_END'
[]
[log_prob_left_flux]
type = LibtorchDRLLogProbabilityPostprocessor
control_name = src_control
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Reporters]
[T_reporter]
type = AccumulateReporter
reporters = 'center_temp_tend/value env_temp/value reward/value left_flux/value log_prob_left_flux/value'
outputs = 'csv_out'
[]
[nn_parameters]
type = LibtorchArtificialNeuralNetParameters
control_name = src_control
outputs = json_out
[]
[]
[Controls]
[src_control]
type = LibtorchDRLControl
parameters = "BCs/left_flux/value"
responses = 'center_temp env_temp'
# keep consistent with LibtorchDRLControlTrainer
input_timesteps = 2
response_scaling_factors = '0.03 0.03'
response_shift_factors = '270 270'
action_standard_deviations = '0.1'
action_scaling_factors = 100
execute_on = 'TIMESTEP_BEGIN'
[]
[]
[Executioner]
type = Transient
solve_type = 'NEWTON'
petsc_options_iname = '-pc_type -pc_factor_shift_type'
petsc_options_value = 'lu NONZERO'
line_search = 'none'
nl_rel_tol = 1e-8
start_time = 0.0
end_time = 86400
dt = 14400.0
[]
[Outputs]
[json_out]
type = JSON
execute_on = FINAL
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/examples/surrogates/cross_validation/all_trainers_uniform_cv.i)
[StochasticTools]
[]
[GlobalParams]
sampler = cv_sampler
response = results/response_data:max:value
cv_type = "k_fold"
cv_splits = 5
cv_n_trials = 100
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[L_dist]
type = Uniform
lower_bound = 0.01
upper_bound = 0.05
[]
[Tinf_dist]
type = Uniform
lower_bound = 290
upper_bound = 310
[]
[]
[Samplers]
[cv_sampler]
type = LatinHypercube
distributions = 'k_dist q_dist L_dist Tinf_dist'
num_rows = 1000
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[cv_sub]
type = SamplerFullSolveMultiApp
input_files = all_sub.i
mode = batch-reset
[]
[]
[Controls]
[pr_cmdline]
type = MultiAppSamplerControl
multi_app = cv_sub
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[response_data]
type = SamplerReporterTransfer
from_multi_app = cv_sub
stochastic_reporter = results
from_reporter = 'max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[cv_scores]
type = CrossValidationScores
models = 'pr_surr pc_surr np_surr gp_surr ann_surr'
execute_on = FINAL
[]
[]
[Trainers]
[pr_max]
type = PolynomialRegressionTrainer
regression_type = "ols"
max_degree = 3
cv_surrogate = "pr_surr"
execute_on = timestep_end
[]
[pc_max]
type = PolynomialChaosTrainer
order = 3
distributions = "k_dist q_dist L_dist Tinf_dist"
cv_surrogate = "pc_surr"
execute_on = timestep_end
[]
[np_max]
type = NearestPointTrainer
cv_surrogate = "np_surr"
execute_on = timestep_end
[]
[gp_max]
type = GaussianProcessTrainer
covariance_function = 'rbf'
standardize_params = 'true'
standardize_data = 'true'
cv_surrogate = "gp_surr"
execute_on = timestep_end
[]
[ann_max]
type = LibtorchANNTrainer
num_epochs = 100
num_batches = 5
num_neurons_per_layer = '64'
learning_rate = 1e-2
rel_loss_tol = 1e-4
filename = mynet.pt
read_from_file = false
print_epoch_loss = 0
activation_function = 'relu'
cv_surrogate = "ann_surr"
standardize_input = false
standardize_output = false
[]
[]
[Covariance]
[rbf]
type = SquaredExponentialCovariance
noise_variance = 3.79e-6
signal_variance = 1 #Use a signal variance of 1 in the kernel
length_factor = '5.34471 1.41191 5.90721 2.83723' #Select a length factor for each parameter
[]
[]
[Surrogates]
[pr_surr]
type = PolynomialRegressionSurrogate
trainer = pr_max
[]
[pc_surr]
type = PolynomialChaos
trainer = pc_max
[]
[np_surr]
type = NearestPointSurrogate
trainer = np_max
[]
[gp_surr]
type = GaussianProcess
trainer = gp_max
[]
[ann_surr]
type = LibtorchANNSurrogate
trainer = ann_max
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/reporters/extra_id_integral/extra_id_integral.i)
[Mesh]
[fmg]
type = FileMeshGenerator
file = 'extra_id_integral.e'
use_for_exodus_restart = true
exodus_extra_element_integers = 'pin_id assembly_id'
[]
[]
[Executioner]
type = Steady
[]
[Problem]
solve = false
[]
[AuxVariables]
[value1]
order = FIRST
initial_from_file_var = value1
[]
[value2]
order = FIRST
initial_from_file_var = value2
[]
[]
[Reporters]
[extra_id_integral]
type = ExtraIDIntegralReporter
variable = 'value1'
id_name = 'assembly_id'
[]
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(test/tests/times/exodus_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[exodus]
type = ExodusFileTimes
files = '../executioners/transient_sync_time/gold/out_tio.e ../executioners/transient_sync_time/gold/out.e'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/reactor/test/tests/positions/cartesian_grid_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
# active = 'cart_grid'
[cart_grid]
type = CartesianGridPositions
center = '0 0 0'
nx = 3
ny = 2
nz = 1
dx = 10
dy = 4
dz = 2
outputs = 'out'
[]
[cart_grid_exclusions_2D]
type = CartesianGridPositions
center = '100 100 0'
nx = 3
ny = 2
nz = 1
dx = 10
dy = 4
dz = 2
pattern = '1 1 1;
2 1 1'
include_in_pattern = '1'
outputs = 'out'
[]
[cart_grid_exclusions_3D]
type = CartesianGridPositions
center = '100 100 0'
nx = 3
ny = 2
nz = 2
dx = 10
dy = 4
dz = 2
pattern = '1 1 1;
2 1 1|
10 1 0;
1 1 0'
include_in_pattern = '1'
outputs = 'out'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(test/tests/reporters/accumulated_reporter/accumulate_reporter.i)
[Mesh/mesh]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Functions/fun]
type = ParsedFunction
expression = 't * x'
[]
[Postprocessors/pp]
type = FunctionValuePostprocessor
function = fun
point = '1 0 0'
execute_on = 'initial timestep_end'
[]
[VectorPostprocessors/vpp]
type = LineFunctionSampler
functions = fun
start_point = '0 0 0'
end_point = '1 0 0'
num_points = 6
sort_by = x
execute_on = 'initial timestep_end'
[]
[Reporters]
[rep]
type = ConstantReporter
dof_id_type_names = 'dofid'
dof_id_type_values = '1'
integer_names = 'int'
integer_values = '1'
string_names = 'str'
string_values = 'two'
integer_vector_names = 'int_vec'
integer_vector_values = '3 4'
string_vector_names = 'str_vec'
string_vector_values = 'five six seven eight'
dof_id_type_vector_names = 'dofid_vec'
dof_id_type_vector_values = '1 2 3'
outputs = none
[]
[accumulate]
type = AccumulateReporter
reporters = 'pp/value vpp/fun rep/int rep/str rep/int_vec rep/str_vec rep/dofid rep/dofid_vec'
[]
[]
[Executioner]
type = Transient
num_steps = 5
# This is just testing that AccumulateReporter doesn't accumulate picard iterations
fixed_point_max_its = 3
custom_pp = pp
direct_pp_value = true
disable_fixed_point_residual_norm_check = true
accept_on_max_fixed_point_iteration = true
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/surrogates/cross_validation/poly_reg_vec.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Normal
mean = 5
standard_deviation = 2
[]
[L_dist]
type = Normal
mean = 0.03
standard_deviation = 0.01
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 10
distributions = 'k_dist L_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[GlobalParams]
sampler = sample
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub_vector.i
mode = batch-reset
execute_on = initial
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Materials/conductivity/prop_values L'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
stochastic_reporter = results
from_reporter = 'T_vec/T T_vec/x'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[cv_scores]
type = CrossValidationScores
models = pr_surrogate
execute_on = FINAL
[]
[]
[Trainers]
[pr_trainer]
type = PolynomialRegressionTrainer
regression_type = "ols"
sampler = sample
response = results/data:T_vec:T
response_type = vector_real
execute_on = timestep_end
max_degree = 1
cv_type = "k_fold"
cv_splits = 2
cv_n_trials = 3
cv_surrogate = pr_surrogate
cv_seed = 1
[]
[]
[Surrogates]
[pr_surrogate]
type = PolynomialRegressionSurrogate
trainer = pr_trainer
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/reporters/declare_initial_setup/declare_initial_setup_with_get.i)
[Mesh]
[generate]
type = GeneratedMeshGenerator
dim = 1
[]
[]
[Variables/u]
[]
[Executioner]
type = Steady
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters]
[initialSetup]
type = TestDeclareInitialSetupReporter
value = 1980
[]
[get]
type = TestGetReporterDeclaredInInitialSetupReporter
other_reporter = initialSetup/value
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_moment.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Normal
mean = 5
standard_deviation = 0.5
[]
[S_dist]
type = Normal
mean = 8
standard_deviation = 0.7
[]
[]
[Samplers]
[quadrature]
type = Quadrature
distributions = 'D_dist S_dist'
execute_on = INITIAL
order = 5
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = quadrature
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = quadrature
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[pc_moments]
type = PolynomialChaosReporter
pc_name = poly_chaos
statistics = 'mean stddev skewness kurtosis'
execute_on = final
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 5
distributions = 'D_dist S_dist'
sampler = quadrature
response = storage/data:avg:value
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/reporters/mesh_info/mesh_info.i)
[Mesh]
parallel_type = DISTRIBUTED
[generate]
type = GeneratedMeshGenerator
dim = 2
nx = 10
ny = 10
[]
# For consistent partitioning across platforms
[Partitioner]
type = GridPartitioner
nx = 2
ny = 1
[]
[]
[Adaptivity]
initial_marker = marker
[Markers/marker]
type = BoxMarker
bottom_left = '0 0 0'
top_right = '1 0.5 0'
inside = 'refine'
outside = 'do_nothing'
[]
[]
[Variables/u]
[]
[Executioner]
type = Steady
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters/mesh_info]
type = MeshInfo
[]
[Outputs]
[out]
type = JSON
[]
[]
(test/tests/times/functor_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[functor]
type = FunctorTimes
functor = 'f1'
[]
[]
[Functions]
[f1]
type = ParsedFunction
expression = '1 + x * 2'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
# Test recover
num_steps = 2
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/combined/examples/stochastic/poly_chaos_uniform.i)
[StochasticTools]
[]
[Distributions]
[cond_inner]
type = Uniform
lower_bound = 20
upper_bound = 30
[]
[cond_outer]
type = Uniform
lower_bound = 90
upper_bound = 110
[]
[heat_source]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[alpha_inner]
type = Uniform
lower_bound = 1e-6
upper_bound = 3e-6
[]
[alpha_outer]
type = Uniform
lower_bound = 5e-7
upper_bound = 1.5e-6
[]
[ymod_inner]
type = Uniform
lower_bound = 2e5
upper_bound = 2.2e5
[]
[ymod_outer]
type = Uniform
lower_bound = 3e5
upper_bound = 3.2e5
[]
[prat_inner]
type = Uniform
lower_bound = 0.29
upper_bound = 0.31
[]
[prat_outer]
type = Uniform
lower_bound = 0.19
upper_bound = 0.21
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 100000
distributions = 'cond_inner cond_outer heat_source alpha_inner alpha_outer ymod_inner ymod_outer prat_inner prat_outer'
execute_on = INITIAL
[]
[]
[Surrogates]
[temp_center_inner]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_temp_center_inner.rd'
[]
[temp_center_outer]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_temp_center_outer.rd'
[]
[temp_end_inner]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_temp_end_inner.rd'
[]
[temp_end_outer]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_temp_end_outer.rd'
[]
[dispx_center_inner]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispx_center_inner.rd'
[]
[dispx_center_outer]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispx_center_outer.rd'
[]
[dispx_end_inner]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispx_end_inner.rd'
[]
[dispx_end_outer]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispx_end_outer.rd'
[]
[dispz_inner]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispz_inner.rd'
[]
[dispz_outer]
type = PolynomialChaos
filename = 'poly_chaos_train_uniform_out_dispz_outer.rd'
[]
[]
[Reporters]
[storage]
type = EvaluateSurrogate
sampler = sample
model = 'temp_center_inner temp_center_outer temp_end_inner temp_end_outer
dispx_center_inner dispx_center_outer dispx_end_inner dispx_end_outer
dispz_inner dispz_outer'
parallel_type = ROOT
[]
[stats]
type = PolynomialChaosReporter
pc_name = 'temp_center_inner temp_center_outer temp_end_inner temp_end_outer
dispx_center_inner dispx_center_outer dispx_end_inner dispx_end_outer
dispz_inner dispz_outer'
statistics = 'mean stddev'
include_sobol = true
[]
[]
[Outputs]
[out]
type = JSON
[]
execute_on = TIMESTEP_END
[]
(test/tests/meshgenerators/parsed_element_deletion_generator/cut_the_small.i)
[Mesh]
[elem1]
type = ElementGenerator
nodal_positions = '0 0 0
1 0 0
1 1 0'
element_connectivity = '0 1 2'
elem_type = "TRI3"
[]
[elem2]
type = ElementGenerator
nodal_positions = '0 0 0
1e-2 0 0
1e-2 1e-2 0'
element_connectivity = '0 1 2'
elem_type = "TRI3"
[]
[combine]
type = CombinerGenerator
inputs = 'elem1 elem2'
[]
[delete]
type = ParsedElementDeletionGenerator
input = combine
expression = 'volume < 0.1'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Reporters]
[mesh]
type = MeshInfo
outputs = json
items = 'num_nodes'
[]
[]
[Outputs]
[json]
type = JSON
execute_system_information_on = NONE
[]
[]
(modules/optimization/test/tests/optimizationreporter/parameter_mesh_base/twoParamMeshOptRep.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = ParameterMeshOptimization
parameter_names = 'parameter1 parameter2'
parameter_meshes = 'parameter_mesh_boundsIC_out.e parameter_mesh_boundsIC_out.e'
parameter_families = 'LAGRANGE MONOMIAL'
parameter_orders = 'FIRST CONSTANT'
num_parameter_times=1
measurement_points = '0.1 0.2 0.3
0.4 0.5 0.6
0.7 0.8 0.9
1.0 1.1 1.2'
measurement_values = '11 12 13 14'
outputs = outjson
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '0 0.5 0.5 0 1 1 0 1 1 0 2 2 0 1.5 1.5 0 3 3 0.25 0.75 0.5 1.5 0.75 2.25'
expected_upper_bounds = '2 2.5 2.5 2 3 3 2 3 3 2 4 4 2 3.5 3.5 2 5 5 2.25 2.75 2.5 3.5 2.75 4.25'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[outjson]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/times/simulation_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[input]
type = InputTimes
times = '0.2 0.4 0.9'
[]
[check_times]
type = SimulationTimes
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
# Test recover
num_steps = 2
[TimeStepper]
type = TimeSequenceFromTimes
times = input
[]
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/stochastic_reporter/stats.i)
[StochasticTools]
auto_create_executioner = false
[]
[Samplers]
[sample]
type = CartesianProduct
execute_on = PRE_MULTIAPP_SETUP
linear_space_items = '0 1 3
0.0 0.1 5'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
execute_on = 'INITIAL TIMESTEP_BEGIN'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = storage
from_reporter = 'pp/value constant/int'
[]
[]
[Controls]
[runner]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Reporters/constant/integer_values
Postprocessors/pp/default'
sampler = sample
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = "none"
[]
[stats]
type = StatisticsReporter
reporters = 'storage/data:pp:value storage/data:constant:int'
compute = mean
[]
[]
[Executioner]
type = Transient
num_steps = 2
dt = 0.01
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/examples/sobol/main.i)
[StochasticTools]
[]
[Distributions]
[gamma]
type = Uniform
lower_bound = 0.5
upper_bound = 2.5
[]
[q_0]
type = Weibull
location = -110
scale = 20
shape = 1
[]
[T_0]
type = Normal
mean = 300
standard_deviation = 45
[]
[s]
type = Normal
mean = 100
standard_deviation = 25
[]
[]
[Samplers]
[hypercube_a]
type = LatinHypercube
num_rows = 10000
distributions = 'gamma q_0 T_0 s'
seed = 2011
[]
[hypercube_b]
type = LatinHypercube
num_rows = 10000
distributions = 'gamma q_0 T_0 s'
seed = 2013
[]
[sobol]
type = Sobol
sampler_a = hypercube_a
sampler_b = hypercube_b
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = sobol
input_files = 'diffusion.i'
mode = batch-restore
[]
[]
[Transfers]
[parameters]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = sobol
parameters = 'Materials/constant/prop_values Kernels/source/value BCs/right/value BCs/left/value'
[]
[results]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = sobol
stochastic_reporter = results
from_reporter = 'T_avg/value q_left/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = 'results/results:T_avg:value results/results:q_left:value'
compute = 'mean'
ci_method = 'percentile'
ci_levels = '0.05 0.95'
[]
[sobol]
type = SobolReporter
sampler = sobol
reporters = 'results/results:T_avg:value results/results:q_left:value'
ci_levels = '0.05 0.95'
[]
[]
[Outputs]
execute_on = 'FINAL'
[out]
type = JSON
[]
[]
(test/tests/misc/solution_invalid/solution_invalid.i)
[Mesh]
type = GeneratedMesh
dim = 2
nx = 2
ny = 2
xmax = 1
ymax = 1
[]
[Variables]
[u]
[]
[]
# Sets solution invalid using the SolutionInvalidInterface, as diffusivity exceeds the set threshold.
[Materials]
[filter]
type = NonsafeMaterial
diffusivity = 0.5
threshold = 0.3
[]
[]
[Kernels]
[diffusion]
type = MatDiffusion
variable = u
diffusivity = diffusivity
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 1
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 0
[]
[]
[Problem]
type = FEProblem
allow_invalid_solution = false
immediately_print_invalid_solution = false
[]
[Executioner]
type = Steady
solve_type = 'NEWTON'
petsc_options_iname = '-pc_type -pc_factor_mat_solver_type'
petsc_options_value = 'lu superlu_dist'
[]
[Reporters/solution_invalidity]
type = SolutionInvalidityReporter
execute_on = FINAL
[]
[Outputs]
file_base = 'solution_invalid'
[out]
type = JSON
execute_on = 'FINAL'
[]
[]
(modules/stochastic_tools/test/tests/reporters/statistics/statistics_main.i)
[StochasticTools]
[]
[Samplers/sample]
type = CartesianProduct
linear_space_items = '1 1 6'
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = sample
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = constant_sub.i
mode = batch-reset
[]
[Controls/param]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'val'
[]
[Transfers/data]
type = SamplerReporterTransfer
from_multi_app = sub
from_reporter = 'const/num const/int const/vec'
stochastic_reporter = 'storage'
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = 'storage/data:const:num storage/data:const:int storage/data:const:vec'
compute = 'min max sum mean stddev norm2 ratio stderr median'
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/surrogates/load_store/evaluate.i)
[StochasticTools]
[]
[Surrogates/poly_chaos]
type = PolynomialChaos
filename = 'train_out_poly_chaos.rd'
[]
[Reporters/pc_data]
type = PolynomialChaosReporter
pc_name = poly_chaos
include_data = true
execute_on = final
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(test/tests/positions/creating_multiapps/apps_from_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[AuxVariables]
[u]
[]
[]
[Positions]
[input]
type = InputPositions
positions = '0 1 0
1 0 2'
outputs = none
[]
[file]
type = FilePositions
files = '../../multiapps/positions_from_file/positions.txt'
outputs = none
[]
[reporter_forward]
type = ReporterPositions
reporters = 'file/positions_1d'
outputs = none
[]
[mesh]
type = ElementCentroidPositions
outputs = none
[]
[results_m1]
type = MultiAppPositions
multiapps = 'm1'
[]
[results_m2]
type = MultiAppPositions
multiapps = 'm2'
[]
[results_m3]
type = MultiAppPositions
multiapps = 'm3'
[]
[results_m4]
type = MultiAppPositions
multiapps = 'm4'
[]
[]
[MultiApps]
[m1]
type = FullSolveMultiApp
input_files = 'apps_from_positions.i'
cli_args = "MultiApps/active='';Positions/active='';Outputs/active=''"
positions_objects = input
[]
[m2]
type = FullSolveMultiApp
input_files = 'apps_from_positions.i'
cli_args = "MultiApps/active='';Positions/active='';Outputs/active=''"
positions_objects = 'input file'
[]
# Those Positions are executed too late
[m3]
type = TransientMultiApp
input_files = 'apps_from_positions.i'
cli_args = "MultiApps/active='';Positions/active='';Outputs/active=''"
positions_objects = reporter_forward
[]
[m4]
type = TransientMultiApp
input_files = 'apps_from_positions.i'
cli_args = "MultiApps/active='';Positions/active='';Outputs/active=''"
positions_objects = mesh
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/surrogates/nearest_point/np_vec.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Normal
mean = 5
standard_deviation = 2
[]
[L_dist]
type = Normal
mean = 0.03
standard_deviation = 0.01
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 10
distributions = 'k_dist L_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[GlobalParams]
sampler = sample
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub_vector.i
mode = batch-reset
execute_on = initial
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Materials/conductivity/prop_values L'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
stochastic_reporter = results
from_reporter = 'T_vec/T T_vec/x'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[eval]
type = EvaluateSurrogate
model = np_surrogate
response_type = vector_real
parallel_type = ROOT
execute_on = timestep_end
[]
[]
[Trainers]
[np]
type = NearestPointTrainer
response = results/data:T_vec:T
response_type = vector_real
execute_on = initial
[]
[]
[Surrogates]
[np_surrogate]
type = NearestPointSurrogate
trainer = np
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(modules/stochastic_tools/test/tests/transfers/libtorch_nn_transfer/libtorch_drl_control_trainer.i)
[StochasticTools]
[]
[Samplers]
[dummy]
type = CartesianProduct
linear_space_items = '0 0.01 1'
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = dummy
input_files = 'libtorch_drl_control_sub.i'
[]
[]
[Transfers]
[nn_transfer]
type = LibtorchNeuralNetControlTransfer
to_multi_app = runner
trainer_name = nn_trainer
control_name = src_control
[]
[r_transfer]
type = MultiAppReporterTransfer
from_multi_app = runner
to_reporters = 'results/center_temp results/env_temp results/reward results/left_flux results/log_prob_left_flux'
from_reporters = 'T_reporter/center_temp_tend:value T_reporter/env_temp:value T_reporter/reward:value T_reporter/left_flux:value T_reporter/log_prob_left_flux:value'
[]
[]
[Trainers]
[nn_trainer]
type = LibtorchDRLControlTrainer
response = 'results/center_temp results/env_temp'
control = 'results/left_flux'
log_probability = 'results/log_prob_left_flux'
reward = 'results/reward'
num_epochs = 10
update_frequency = 2
decay_factor = 0.0
loss_print_frequency = 3
critic_learning_rate = 0.0005
num_critic_neurons_per_layer = '4 2'
control_learning_rate = 0.0005
num_control_neurons_per_layer = '4 2'
# keep consistent with LibtorchNeuralNetControl
input_timesteps = 2
response_scaling_factors = '0.03 0.03'
response_shift_factors = '270 270'
action_standard_deviations = '0.1'
read_from_file = false
[]
[]
[Reporters]
[results]
type = ConstantReporter
real_vector_names = 'center_temp env_temp reward left_flux log_prob_left_flux'
real_vector_values = '0; 0; 0; 0; 0;'
outputs = 'csv_out'
execute_on = timestep_begin
[]
[nn_parameters]
type = DRLControlNeuralNetParameters
trainer_name = nn_trainer
outputs = json_out
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
file_base = train_out
[json_out]
type = JSON
execute_on = TIMESTEP_BEGIN
execute_system_information_on = NONE
[]
[]
(test/tests/reporters/perf_graph_reporter/perf_graph_reporter_recover.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 1
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 2
[]
[Reporters/perf_graph]
type = PerfGraphReporter
execute_on = FINAL
[]
[Outputs/json]
type = JSON
execute_on = FINAL
[]
(modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_groupBounds.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = OptimizationReporter
parameter_names = 'p1 p2 p3'
num_values = '2 4 6'
initial_condition = '1; 3; 7'
upper_bounds = '110; 210; 310'
lower_bounds = '-1; -2; -3'
measurement_file = 'measurementData.csv'
file_xcoord = 'coordx'
file_ycoord ='y'
file_zcoord = 'z'
file_value = 'measured_value'
outputs = out
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 70 80 90 100 110 120'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '-1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -3 -3'
expected_upper_bounds = '110 110 210 210 210 210 310 310 310 310 310 310'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/positions/transformed_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[input]
type = InputPositions
positions = '0 0 0
1 0 0
0 1 0'
outputs = none
[]
[scale]
type = TransformedPositions
base_positions = 'input'
vector_value = '1 2 3'
transform = 'SCALE'
[]
[rotate]
type = TransformedPositions
base_positions = 'input'
vector_value = '0 0 90'
transform = 'ROTATE_XYZ'
[]
[translate]
type = TransformedPositions
base_positions = 'input'
vector_value = '10 0 0'
transform = 'TRANSLATE'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/ActiveLearningGP/main_adam.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 5
upper_bound = 20
[]
[q_dist]
type = Uniform
lower_bound = 7000
upper_bound = 13000
[]
[Tinf_dist]
type = Uniform
lower_bound = 250
upper_bound = 350
[]
[]
[Samplers]
[mc]
type = ActiveLearningMonteCarloSampler
num_batch = 1
distributions = 'k_dist q_dist Tinf_dist'
flag_sample = 'conditional/flag_sample'
seed = 5
num_samples = 20
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
sampler = mc
input_files = 'sub.i'
mode = batch-reset
should_run_reporter = conditional/need_sample
execute_on = TIMESTEP_END
[]
[]
[Transfers]
[sub]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = mc
parameters = 'Materials/conductivity/prop_values Kernels/source/value BCs/right/value'
to_control = 'stochastic'
check_multiapp_execute_on = false
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'avg/value'
stochastic_reporter = 'conditional'
from_multi_app = sub
sampler = mc
[]
[]
[Reporters]
[conditional]
type = ActiveLearningGPDecision
sampler = mc
parallel_type = ROOT
execute_on = 'timestep_begin'
flag_sample = 'flag_sample'
inputs = 'inputs'
gp_mean = 'gp_mean'
gp_std = 'gp_std'
n_train = 6
al_gp = GP_al_trainer
gp_evaluator = GP_eval
learning_function = 'Ufunction'
learning_function_parameter = 349.345
learning_function_threshold = 2.0
[]
[]
[Trainers]
[GP_al_trainer]
type = ActiveLearningGaussianProcess
covariance_function = 'covar'
standardize_params = 'true'
standardize_data = 'true'
tune_parameters = 'signal_variance length_factor'
tuning_algorithm = 'adam'
iter_adam = 1000
learning_rate_adam = 0.005
# show_optimization_details = true
[]
[]
[Surrogates]
[GP_eval]
type = GaussianProcess
trainer = GP_al_trainer
[]
[]
[Covariance]
[covar]
type = SquaredExponentialCovariance
signal_variance = 1.0
noise_variance = 1e-4
length_factor = '1.0 1.0 1.0'
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
file_base = 'single_proc_single_row_ufunction'
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/meshgenerators/mesh_repair_generator/mixed_elements.i)
[Mesh]
[dir1]
type = ElementGenerator
nodal_positions = '0 0 0
1 0 0
0 1 0'
element_connectivity = '0 1 2'
elem_type = 'TRI3'
[]
[dir2]
type = ElementGenerator
nodal_positions = '0 0 0
1 0 0
1 1 0
0 1 0'
element_connectivity = '0 1 2 3'
elem_type = 'QUAD4'
[]
[combine]
type = CombinerGenerator
inputs = 'dir1 dir2'
[]
[separate]
type = MeshRepairGenerator
input = 'combine'
separate_blocks_by_element_types = true
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Reporters]
[mesh]
type = MeshInfo
outputs = json
items = 'subdomains'
[]
[]
[Outputs]
[json]
type = JSON
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/test/tests/reporters/AISActiveLearning/ais_al.i)
[StochasticTools]
[]
[Distributions]
[mu1]
type = Normal
mean = 0.0
standard_deviation = 0.5
[]
[mu2]
type = Normal
mean = 1
standard_deviation = 0.5
[]
[]
[Samplers]
[sample]
type = AISActiveLearning
distributions = 'mu1 mu2'
proposal_std = '1.0 1.0'
output_limit = 0.65
num_samples_train = 15
num_importance_sampling_steps = 5
std_factor = 0.9
initial_values = '-0.103 1.239'
inputs_reporter = 'adaptive_MC/inputs'
use_absolute_value = true
flag_sample = 'conditional/flag_sample'
seed = 9874
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-reset
should_run_reporter = conditional/need_sample
execute_on = TIMESTEP_END
[]
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value BCs/right/value'
to_control = 'stochastic'
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'conditional'
from_multi_app = sub
sampler = sample
[]
[]
[Reporters]
[conditional]
type = ActiveLearningGPDecision
sampler = sample
parallel_type = ROOT
execute_on = 'initial timestep_begin'
flag_sample = 'flag_sample'
inputs = 'inputs'
gp_mean = 'gp_mean'
gp_std = 'gp_std'
n_train = 5
al_gp = GP_al_trainer
gp_evaluator = GP_eval
learning_function='Ufunction'
learning_function_parameter = 0.65
learning_function_threshold = 2.0
[]
[adaptive_MC]
type = AdaptiveMonteCarloDecision
output_value = conditional/gp_mean
inputs = 'inputs'
sampler = sample
gp_decision = conditional
[]
[ais_stats]
type = AdaptiveImportanceStats
output_value = conditional/gp_mean
sampler = sample
flag_sample = 'conditional/flag_sample'
[]
[]
[Trainers]
[GP_al_trainer]
type = ActiveLearningGaussianProcess
covariance_function = 'covar'
standardize_params = 'true'
standardize_data = 'true'
tune_parameters = 'signal_variance length_factor'
tuning_algorithm = 'adam'
iter_adam = 2000
learning_rate_adam = 0.005
# show_optimization_details = true
[]
[]
[Surrogates]
[GP_eval]
type = GaussianProcess
trainer = GP_al_trainer
[]
[]
[Covariance]
[covar]
type= SquaredExponentialCovariance
signal_variance = 1.0
noise_variance = 1e-8
length_factor = '1.0 1.0'
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
file_base = 'ais_al'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/test/tests/samplers/dynamic_size/main.i)
[StochasticTools]
[]
[Distributions]
[uniform]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[]
[Samplers]
[dynamic]
type = TestDynamicNumberOfSubAppsSampler
num_rows = 5
distributions = 'uniform'
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Executioner]
type = Transient
num_steps = 2
[]
[VectorPostprocessors]
[sample]
type = SamplerData
sampler = dynamic
execute_on = 'INITIAL TIMESTEP_END'
[]
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
[]
(test/tests/meshgenerators/rename_boundary_generator/name_and_id.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
nx = 2
ny = 2
xmin = 0
xmax = 4
ymin = 0
ymax = 4
[]
[SubdomainBoundingBox]
type = SubdomainBoundingBoxGenerator
input = gmg
block_id = 1
bottom_left = '0 0 0'
top_right = '2 2 2'
[]
[ed0]
type = BlockDeletionGenerator
input = SubdomainBoundingBox
block = 1
# this makes a new boundary with an ID of 100 and a name of "100"
new_boundary = '100'
[]
[rename_both_id_and_name]
type = RenameBoundaryGenerator
input = ed0
old_boundary = '100' # this is both an ID and a name, which we want to both rename
new_boundary = '101'
[]
# We compare by element numbers, which are not consistent in parallel
# if this is true
allow_renumbering = false
parallel_type = replicated
[]
[Reporters/mesh_info]
type = MeshInfo
items = sideset_elems
[]
[Outputs]
[out]
type = JSON
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
(modules/stochastic_tools/test/tests/surrogates/load_store/train_and_evaluate.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[S_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[]
[Samplers]
[quadrature]
type = Quadrature
distributions = 'D_dist S_dist'
execute_on = INITIAL
order = 5
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = quadrature
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = quadrature
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
outputs = none
[]
[pc_data]
type = PolynomialChaosReporter
pc_name = poly_chaos
include_data = true
execute_on = final
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 5
distributions = 'D_dist S_dist'
sampler = quadrature
response = storage/data:avg:value
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(modules/optimization/test/tests/optimizationreporter/general_opt/main.i)
[Optimization]
[]
[OptimizationReporter]
type = GeneralOptimization
parameter_names = 'vals'
num_values = '2'
objective_name = obj_value
[]
[Problem]
solve = false
[]
[Executioner]
type = Optimize
tao_solver = taobqnktr
petsc_options_iname = '-tao_gatol'
petsc_options_value = '1e-8 '
verbose = true
[]
[MultiApps]
[forward]
type = FullSolveMultiApp
input_files = forward.i
execute_on = FORWARD
[]
[]
[Transfers]
[toForward]
type = MultiAppReporterTransfer
to_multi_app = forward
from_reporters = 'OptimizationReporter/vals'
to_reporters = 'vals/vals'
[]
[fromForward]
type = MultiAppReporterTransfer
from_multi_app = forward
from_reporters = 'obj_pp/value
grad_f/grad_f'
to_reporters = 'OptimizationReporter/obj_value
OptimizationReporter/grad_vals'
[]
[]
[Outputs]
[json]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter_ci.i)
[StochasticTools]
[]
[Reporters]
[const]
type = ConstantReporter
real_vector_names = 'const'
real_vector_values = '2 4 6 8 10'
[]
[stats]
type = StatisticsReporter
reporters = 'const/const'
compute = 'min max sum mean stddev norm2 ratio stderr median'
ci_method = percentile
[]
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(modules/stochastic_tools/test/tests/samplers/AdaptiveImportanceSampler/ais.i)
[StochasticTools]
[]
[Distributions]
[mu1]
type = Normal
mean = 0.0
standard_deviation = 0.5
[]
[mu2]
type = Normal
mean = 1
standard_deviation = 0.5
[]
[]
[Samplers]
[sample]
type = AdaptiveImportance
distributions = 'mu1 mu2'
proposal_std = '1.0 1.0'
output_limit = 0.65
num_samples_train = 30
num_importance_sampling_steps = 30
std_factor = 0.9
initial_values = '-0.103 1.239'
inputs_reporter = 'adaptive_MC/inputs'
use_absolute_value = true
seed = 1012
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'BCs/left/value BCs/right/value'
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[adaptive_MC]
type = AdaptiveMonteCarloDecision
output_value = constant/reporter_transfer:average:value
inputs = 'inputs'
sampler = sample
[]
[ais_stats]
type = AdaptiveImportanceStats
output_value = constant/reporter_transfer:average:value
sampler = sample
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/parallel_storage/parallel_storage_main.i)
[StochasticTools]
[]
[Distributions]
[S_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[D_dist]
type = Uniform
lower_bound = 0
upper_bound = 10
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 4
distributions = 'S_dist D_dist'
min_procs_per_row = 2
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[worker]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-reset
min_procs_per_app = 2
[]
[]
[Transfers]
[param_transfer]
type = SamplerParameterTransfer
to_multi_app = worker
sampler = sample
parameters = 'Kernels/source_u/value BCs/right_v/value'
[]
[solution_transfer]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage
variables = 'u v'
serialize_on_root = false
[]
[solution_transfer_aux]
type = SerializedSolutionTransfer
parallel_storage = parallel_storage
from_multi_app = worker
sampler = sample
solution_container = solution_storage_aux
variables = 'u_aux'
serialize_on_root = false
[]
[]
[Controls]
[cmd_line]
type = MultiAppSamplerControl
multi_app = worker
sampler = sample
param_names = 'S D'
[]
[]
[Reporters]
[parallel_storage]
type = ParallelSolutionStorage
variables = 'u v u_aux'
outputs = out
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_distributed.i)
[StochasticTools]
[]
[VectorPostprocessors]
[data]
type = TestDistributedVectorPostprocessor
parallel_type = distributed
outputs = none
[]
[]
[Reporters]
[stats]
type = StatisticsReporter
vectorpostprocessors = 'data'
compute = 'mean'
ci_method = 'percentile'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 10
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/ActiveLearningGP/main_tao.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 5
upper_bound = 20
[]
[q_dist]
type = Uniform
lower_bound = 7000
upper_bound = 13000
[]
[Tinf_dist]
type = Uniform
lower_bound = 250
upper_bound = 350
[]
[]
[Samplers]
[mc]
type = ActiveLearningMonteCarloSampler
num_batch = 1
distributions = 'k_dist q_dist Tinf_dist'
flag_sample = 'conditional/flag_sample'
seed = 12
num_samples = 20
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
sampler = mc
input_files = 'sub.i'
mode = batch-reset
should_run_reporter = conditional/need_sample
execute_on = TIMESTEP_END
[]
[]
[Transfers]
[sub]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = mc
parameters = 'Materials/conductivity/prop_values Kernels/source/value BCs/right/value'
to_control = 'stochastic'
check_multiapp_execute_on = false
[]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'avg/value'
stochastic_reporter = 'conditional'
from_multi_app = sub
sampler = mc
[]
[]
[Reporters]
[conditional]
type = ActiveLearningGPDecision
sampler = mc
parallel_type = ROOT
execute_on = 'initial timestep_begin'
flag_sample = 'flag_sample'
inputs = 'inputs'
gp_mean = 'gp_mean'
gp_std = 'gp_std'
n_train = 6
al_gp = GP_al_trainer
gp_evaluator = GP_eval
learning_function = 'Ufunction'
learning_function_parameter = 349.345
learning_function_threshold = 2.0
[]
[]
[Trainers]
[GP_al_trainer]
type = ActiveLearningGaussianProcess
covariance_function = 'covar'
standardize_params = 'true'
standardize_data = 'true'
tune_parameters = 'signal_variance length_factor'
tao_options = '-tao_bncg_type ssml_bfgs'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
# show_optimization_details = true
[]
[]
[Surrogates]
[GP_eval]
type = GaussianProcess
trainer = GP_al_trainer
[]
[]
[Covariance]
[covar]
type = SquaredExponentialCovariance
signal_variance = 1.0
noise_variance = 1e-3
length_factor = '1.0 1.0 1.0'
[]
[]
[Executioner]
type = Transient
[]
[Outputs]
# perf_graph = true
file_base = 'ufunction_tao'
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/outputs/pp_as_reporter/pp_as_reporter.i)
[Mesh/gen]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Postprocessors/data]
type = FunctionValuePostprocessor
function = 1980
[]
[Outputs]
[out]
type = JSON
postprocessors_as_reporters = true
[]
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_distributed.i)
[StochasticTools]
[]
[VectorPostprocessors]
[data]
type = TestDistributedVectorPostprocessor
parallel_type = distributed
outputs = none
[]
[]
[Reporters]
[stats]
type = StatisticsReporter
vectorpostprocessors = 'data'
compute = 'mean'
ci_method = 'bca'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.99'
ci_replicates = 10
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile.i)
[StochasticTools]
[]
[VectorPostprocessors]
# Table 2.1 in Efron and Tibshirani, 1993
[treatment]
type = ConstantVectorPostprocessor
value = '94 197 16 38 99 141 23'
outputs = none
[]
[control]
type = ConstantVectorPostprocessor
value = '52 104 146 10 51 30 40 27 46'
outputs = none
[]
[]
[Reporters]
# Reproduce Table 13.1 in Efron and Tibshirani, 1993
[stats]
type = StatisticsReporter
vectorpostprocessors = 'treatment control'
compute = 'mean stderr'
ci_method = 'percentile'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 1000
ci_seed = 1980
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(modules/reactor/test/tests/positions/hexagonal_grid_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
active = 'hex_grid'
[hex_grid]
type = HexagonalGridPositions
center = '0 0 0'
nr = 2
# the lattice flat to flat is very large compared to the pitch * nr
# the duct would be in between the lattice and the lattice
lattice_flat_to_flat = 7
pin_pitch = 0.5
outputs = 'out'
[]
[hex_grid_2rings_exclusions]
type = HexagonalGridPositions
center = '0 0 0'
nr = 2
pattern = '1 1;
2 1 2;
1 1'
include_in_pattern = '1'
lattice_flat_to_flat = 4
pin_pitch = 0.5
outputs = 'out'
[]
[hex_grid_3rings_exclusions]
type = HexagonalGridPositions
center = '0 0 0'
nr = 3
pattern = '1 1 1;
1 1 1 1;
2 1 3 2 3;
1 1 3 1;
1 1 1'
include_in_pattern = '1 2'
lattice_flat_to_flat = 4
pin_pitch = 0.5
outputs = 'out'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_moment.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[S_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[]
[Samplers]
[quadrature]
type = Quadrature
distributions = 'D_dist S_dist'
execute_on = INITIAL
order = 5
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = quadrature
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = quadrature
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[pc_moments]
type = PolynomialChaosReporter
pc_name = poly_chaos
statistics = 'mean stddev skewness kurtosis'
execute_on = final
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 5
distributions = 'D_dist S_dist'
sampler = quadrature
response = storage/data:avg:value
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/positions/multiapp_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[multiapp]
type = MultiAppPositions
multiapps = 'm1 m2 m3'
[]
[]
[MultiApps]
[m1]
type = CentroidMultiApp
input_files = 'multiapp_positions.i'
cli_args = "MultiApps/active='';Positions/active=''"
[]
[m2]
type = FullSolveMultiApp
input_files = 'multiapp_positions.i'
cli_args = "MultiApps/active='';Positions/active=''"
[]
[m3]
type = FullSolveMultiApp
input_files = 'multiapp_positions.i'
positions = '0.2 0.4 1
0 0 6'
cli_args = "MultiApps/active='';Positions/active=''"
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_surr.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[L_dist]
type = Uniform
lower_bound = 0.01
upper_bound = 0.05
[]
[Tinf_dist]
type = Uniform
lower_bound = 290
upper_bound = 310
[]
[]
[Samplers]
[sample]
type = LatinHypercube
num_rows = 100000
distributions = 'k_dist q_dist L_dist Tinf_dist'
[]
[]
[Surrogates]
[pc_max]
type = PolynomialChaos
filename = 'uniform_train_pc_out_pc_max.rd'
[]
[pr_max]
type = PolynomialRegressionSurrogate
filename = 'uniform_train_pr_out_pr_max.rd'
[]
[]
# Computing statistics
[Reporters]
[pc_max_res]
type = EvaluateSurrogate
model = pc_max
sampler = sample
parallel_type = ROOT
[]
[pr_max_res]
type = EvaluateSurrogate
model = pr_max
sampler = sample
parallel_type = ROOT
[]
[pr_max_stats]
type = StatisticsReporter
reporters = 'pr_max_res/pr_max'
compute = 'mean stddev'
[]
[pc_max_stats]
type = PolynomialChaosReporter
pc_name = 'pc_max'
statistics = 'mean stddev'
[]
[]
[Outputs]
[out]
type = JSON
execute_on = timestep_end
[]
[]
(modules/stochastic_tools/test/tests/multiapps/transient_with_full_solve/main.i)
[StochasticTools]
[]
[Distributions]
[uniform]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[]
[Samplers]
[dynamic]
type = MonteCarlo
num_rows = 5
distributions = 'uniform'
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
sampler = dynamic
input_files = 'sub.i'
[]
[]
[Transfers]
[parameters]
type = SamplerParameterTransfer
to_multi_app = runner
sampler = dynamic
parameters = 'BCs/right/value'
[]
[results]
type = SamplerPostprocessorTransfer
from_multi_app = runner
sampler = dynamic
to_vector_postprocessor = results
from_postprocessor = 'center'
[]
[]
[Executioner]
type = Transient
num_steps = 2
[]
[VectorPostprocessors]
[results]
type = StochasticResults
[]
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/sobol.i)
[StochasticTools]
[]
[Distributions/uniform]
type = Uniform
lower_bound = 0
upper_bound = 1
[]
[Samplers/sample]
type = Quadrature
order = 4
distributions = 'uniform uniform uniform uniform uniform uniform'
execute_on = 'initial'
[]
[VectorPostprocessors]
[results]
type = GFunction
sampler = sample
q_vector = '0 0.5 3 9 99 99'
execute_on = INITIAL
outputs = none
[]
[]
[Reporters]
[sobol]
type = PolynomialChaosReporter
pc_name = poly_chaos
include_sobol = true
execute_on = timestep_end
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
distributions = 'uniform uniform uniform uniform uniform uniform'
sampler = sample
response = results/g_values
[]
[]
[Outputs]
execute_on = 'FINAL'
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/transfers/sampler_reporter/main.i)
[StochasticTools]
auto_create_executioner = false
[]
[Samplers]
[sample]
type = CartesianProduct
execute_on = PRE_MULTIAPP_SETUP
linear_space_items = '1 1 2
0.1 0.1 2
0 1e-8 2'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
execute_on = 'INITIAL TIMESTEP_BEGIN'
ignore_solve_not_converge = true
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = storage
from_reporter = 'pp/value vpp/vec constant/str constant/int'
[]
[]
[Controls]
[runner]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Reporters/constant/integer_values
real_val
Executioner/nl_rel_tol'
sampler = sample
[]
[]
[Reporters]
[storage]
type = StochasticReporter
execute_on = 'initial timestep_end'
parallel_type = ROOT
[]
[]
[Executioner]
type = Transient
num_steps = 2
dt = 0.01
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/morris/morris.i)
[StochasticTools]
[]
[Distributions/dist]
type = Uniform
lower_bound = 0
upper_bound = 1
[]
[Samplers/morris]
type = MorrisSampler
distributions = 'dist dist dist dist dist dist'
trajectories = 1024
levels = 4
[]
[VectorPostprocessors/gfun]
type = GFunction
sampler = morris
q_vector = '0 0.5 3 9 99 99'
parallel_type = DISTRIBUTED
execute_on = initial
[]
[Reporters/stat]
type = MorrisReporter
sampler = morris
vectorpostprocessors = 'gfun'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 1000
execute_on = initial
[]
[Outputs]
[out]
type = JSON
execute_on = initial
[]
[]
(modules/stochastic_tools/test/tests/surrogates/cross_validation/train_and_cv.i)
[StochasticTools]
[]
[Samplers]
[sample]
type = CartesianProduct
linear_space_items = '0 0.1515151515 100'
[]
[]
[VectorPostprocessors]
[values]
type = GFunction
sampler = sample
q_vector = '0'
execute_on = INITIAL
outputs = none
[]
[]
[Surrogates]
[surrogate]
type = PolynomialRegressionSurrogate
trainer = train
[]
[]
[Trainers]
[train]
type = PolynomialRegressionTrainer
regression_type = "ols"
sampler = sample
response = values/g_values
max_degree = 3
cv_type = "k_fold"
cv_splits = 2
cv_n_trials = 3
[]
[]
[Reporters]
[cv_scores]
type = CrossValidationScores
models = 'surrogate'
execute_on = FINAL
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(test/tests/transfers/multiapp_reporter_transfer/clone_type.i)
[Mesh/generate]
type = GeneratedMeshGenerator
dim = 1
[]
[Problem]
kernel_coverage_check = false
solve = false
[]
[Reporters]
[receiver]
type = ConstantReporter
[]
[]
[MultiApps]
[multi_reporter]
type = TransientMultiApp
input_files = 'sub0.i sub0.i sub0.i sub0.i'
positions = '0 0 0
0 0 0
0 0 0
0 0 0'
cli_args = 'Postprocessors/from_sub_pp/default=3.1415926;Reporters/from_sub_rep/integer_values=10;Reporters/from_sub_rep/string_values=ten;Outputs/active=""
Postprocessors/from_sub_pp/default=1.5707963;Reporters/from_sub_rep/integer_values=11;Reporters/from_sub_rep/string_values=twenty;Outputs/active=""
Postprocessors/from_sub_pp/default=1.0471975;Reporters/from_sub_rep/integer_values=12;Reporters/from_sub_rep/string_values=thirty;Outputs/active=""
Postprocessors/from_sub_pp/default=0.7853981;Reporters/from_sub_rep/integer_values=13;Reporters/from_sub_rep/string_values=forty;Outputs/active=""'
max_procs_per_app = 1
[]
[]
[Transfers]
[multi_rep]
type = MultiAppCloneReporterTransfer
from_reporters = 'from_sub_pp/value from_sub_rep/int from_sub_rep/str'
to_reporter = receiver
from_multi_app = multi_reporter
reporter_type = 'real integer string'
[]
[]
[Executioner]
type = Transient
num_steps = 1
[]
[Outputs]
[out]
type = JSON
vectorpostprocessors_as_reporters = true
[]
execute_on = timestep_end
[]
(modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_mixBounds.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = OptimizationReporter
parameter_names = 'p1 p2 p3'
num_values = '2 4 6'
initial_condition = '1 2; 3; 7 8 9 10 11 12'
upper_bounds = '101; 103; 107 108 109 110 111 112'
lower_bounds = '-1 -2; -3 -4 -5 -6; -7'
measurement_file = 'measurementData.csv'
file_xcoord = 'coordx'
file_ycoord ='y'
file_zcoord = 'z'
file_value = 'measured_value'
outputs = out
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 70 80 90 100 110 120'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '-1 -2 -3 -4 -5 -6 -7 -7 -7 -7 -7 -7'
expected_upper_bounds = '101 101 103 103 103 103 107 108 109 110 111 112'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(test/tests/meshgenerators/boundary_deletion_generator/boundary_deletion.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
nx = 2
ny = 2
[]
[boundary_removal]
type = BoundaryDeletionGenerator
input = gmg
boundary_names = 'right top'
[]
allow_renumbering = false
[]
[Reporters/mesh_info]
type = MeshInfo
items = sideset_elems
[]
[Outputs]
[out]
type = JSON
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
(modules/optimization/test/tests/optimizationreporter/optimization_reporter_base/optRep_fromCsv_paramBounds.i)
[Mesh]
type = GeneratedMesh
dim = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[null]
type = NullKernel
variable = u
[]
[]
[OptimizationReporter]
type = OptimizationReporter
parameter_names = 'p1 p2 p3'
num_values = '2 4 6'
initial_condition = '1 2; 3 4 5 6; 7 8 9 10 11 12'
upper_bounds = '101 102; 103 104 105 106; 107 108 109 110 111 112'
lower_bounds = '-1 -2; -3 -4 -5 -6; -7 -8 -9 -10 -11 -12'
measurement_file = 'measurementData.csv'
file_xcoord = 'coordx'
file_ycoord ='y'
file_zcoord = 'z'
file_value = 'measured_value'
outputs = out
[]
[UserObjects]
[optReporterTester]
type = OptimizationReporterTest
values_to_set_parameters_to = '10 20 30 40 50 60 70 80 90 100 110 120'
values_to_set_simulation_measurements_to = '111 212 313 314'
expected_objective_value = 115000
expected_lower_bounds = '-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12'
expected_upper_bounds = '101 102 103 104 105 106 107 108 109 110 111 112'
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/sobol/sobol.i)
[StochasticTools]
[]
[Distributions/uniform]
type = Uniform
lower_bound = 0
upper_bound = 1
[]
[Samplers]
[sample]
type = MonteCarlo
distributions = 'uniform uniform uniform uniform uniform uniform'
num_rows = 1024
seed = 0
[]
[resample]
type = MonteCarlo
distributions = 'uniform uniform uniform uniform uniform uniform'
num_rows = 1024
seed = 1
[]
[sobol]
type = Sobol
sampler_a = sample
sampler_b = resample
[]
[]
[VectorPostprocessors]
[results]
type = GFunction
sampler = sobol
q_vector = '0 0.5 3 9 99 99'
execute_on = INITIAL
outputs = none
parallel_type = DISTRIBUTED
[]
[]
[Reporters]
[sobol]
type = SobolReporter
sampler = sobol
vectorpostprocessors = results
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 1000
execute_on = FINAL
[]
[]
[Outputs]
execute_on = 'FINAL'
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/samplers/mcmc/main_des_var.i)
[StochasticTools]
[]
[Distributions]
[left]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[right]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[variance]
type = Uniform
lower_bound = 0.0
upper_bound = 0.5
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'mcmc_reporter/noise'
file_name = 'exp_0_05.csv'
log_likelihood = true
[]
[]
[Samplers]
[sample]
type = AffineInvariantDES
prior_distributions = 'left right'
num_parallel_proposals = 5
file_name = 'confg.csv'
execute_on = PRE_MULTIAPP_SETUP
seed = 2547
initial_values = '0.1 0.1'
previous_state = 'mcmc_reporter/inputs'
previous_state_var = 'mcmc_reporter/variance'
prior_variance = 'variance'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
sampler = sample
param_names = 'left_bc right_bc mesh1'
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[mcmc_reporter]
type = AffineInvariantDifferentialDecision
output_value = constant/reporter_transfer:average:value
sampler = sample
likelihoods = 'gaussian'
[]
[]
[Executioner]
type = Transient
num_steps = 5
[]
[Outputs]
file_base = 'des_5prop_var'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/misc/execute_on/execute_on_test.i)
[Mesh]
type = GeneratedMesh
dim = 2
xmax = 1
ymax = 1
[]
[Variables]
[u]
[]
[]
[Kernels]
[diff]
type = Diffusion
variable = u
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = 1
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = 2
value = 1
[]
[]
[Reporters]
[initial]
type = CurrentExecFlagReporter
execute_on = initial
[]
[timestep_begin]
type = CurrentExecFlagReporter
execute_on = timestep_begin
[]
[timestep_end]
type = CurrentExecFlagReporter
execute_on = timestep_end
[]
[nonlinear]
type = CurrentExecFlagReporter
execute_on = nonlinear
[]
[linear]
type = CurrentExecFlagReporter
execute_on = linear
[]
[custom]
type = CurrentExecFlagReporter
execute_on = custom
[]
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
[]
[]
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_main.i)
[StochasticTools]
[]
[Distributions]
[n0]
type = Normal
mean = 0
standard_deviation = 1
[]
[n1]
type = Normal
mean = 1
standard_deviation = 1
[]
[n2]
type = Normal
mean = 2
standard_deviation = 0.5
[]
[n3]
type = Normal
mean = 3
standard_deviation = 0.33333333333
[]
[n4]
type = Normal
mean = 4
standard_deviation = 0.25
[]
[]
[Samplers/sample]
type = MonteCarlo
distributions = 'n0 n1 n2 n3 n4'
num_rows = 100
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = sample
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
mode = batch-reset
[]
[Controls/param]
type = MultiAppSamplerControl
multi_app = sub
param_names = 'Reporters/const/real_vector_values[0,1,2,3,4]'
[]
[Transfers/data]
type = SamplerReporterTransfer
from_multi_app = sub
from_reporter = 'const/num_vec'
stochastic_reporter = storage
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[stats]
type = StatisticsReporter
reporters = storage/data:const:num_vec
compute = 'mean stddev'
ci_method = 'bca'
ci_levels = '0.025 0.05 0.1 0.16 0.5 0.84 0.9 0.95 0.975'
ci_replicates = 10000
ci_seed = 1945
execute_on = FINAL
[]
[]
[Outputs]
execute_on = FINAL
[out]
type = JSON
[]
[]
(test/tests/positions/element_group_centroid_positions.i)
[Mesh]
# Need additional reductions in code for distributed
parallel_type = replicated
[fmg]
type = FileMeshGenerator
file = 'depletion_id_in.e'
exodus_extra_element_integers = 'material_id pin_id assembly_id'
[]
[]
[Positions]
[all_mesh_blocks]
type = ElementGroupCentroidPositions
grouping_type = 'block'
[]
[block_1]
type = ElementGroupCentroidPositions
block = 1
grouping_type = 'block'
[]
[block_and_one_id]
type = ElementGroupCentroidPositions
block = '1 2'
extra_id_name = 'pin_id'
extra_id = '1 2 4'
grouping_type = 'block_and_extra_id'
[]
[block_and_two_id]
type = ElementGroupCentroidPositions
block = '1 2'
extra_id_name = 'assembly_id pin_id'
extra_id = '1; 1 2 4'
grouping_type = 'block_and_extra_id'
[]
[block_and_three_id]
type = ElementGroupCentroidPositions
block = '1 2'
extra_id_name = 'assembly_id pin_id material_id'
extra_id = '0; 1 2 4 6; ;'
grouping_type = 'block_and_extra_id'
[]
[three_ids]
type = ElementGroupCentroidPositions
extra_id_name = 'assembly_id pin_id material_id'
extra_id = '0; 1 2 4 6; ;'
grouping_type = 'extra_id'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/test/tests/reporters/basic/statistics_reporter.i)
[StochasticTools]
[]
[Reporters]
[const]
type = ConstantReporter
real_vector_names = 'real'
real_vector_values = '2 4 6 8 10'
integer_vector_names = 'int'
integer_vector_values = '100 200 300 400 500'
[]
[stats]
type = StatisticsReporter
reporters = 'const/real const/int'
compute = 'min max sum mean stddev norm2 ratio stderr'
[]
[]
[Outputs/out]
type = JSON
execute_on = FINAL
[]
(test/tests/meshgenerators/rename_boundary_generator/rename_boundary.i)
[Mesh]
[gmg]
type = GeneratedMeshGenerator
dim = 2
nx = 2
ny = 2
[]
# Rename parameters supplied through the "tests" specifications
[rename]
type = RenameBoundaryGenerator
input = gmg
[]
# We compare by element numbers, which are not consistent in parallel
# if this is true
allow_renumbering = false
[]
[Reporters/mesh_info]
type = MeshInfo
items = sideset_elems
[]
[Outputs]
[out]
type = JSON
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_locs.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[S_dist]
type = Uniform
lower_bound = 2.5
upper_bound = 7.5
[]
[]
[Samplers]
[grid]
type = CartesianProduct
linear_space_items = '2.5 0.5 10 2.5 0.5 10'
[]
[quadrature]
type = Quadrature
distributions = 'D_dist S_dist'
execute_on = INITIAL
order = 5
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = quadrature
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = quadrature
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[local_sense]
type = PolynomialChaosReporter
pc_name = poly_chaos
local_sensitivity_sampler = grid
local_sensitivity_points = '3.14159 3.14159 2.7182 3.14159 3.14159 2.7182 2.7182 2.7182'
execute_on = final
[]
[]
[Surrogates]
[poly_chaos]
type = PolynomialChaos
trainer = poly_chaos
[]
[]
[Trainers]
[poly_chaos]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 5
distributions = 'D_dist S_dist'
sampler = quadrature
response = storage/data:avg:value
[]
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/samplers/mcmc/main_imh.i)
[StochasticTools]
[]
[Distributions]
[left]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[right]
type = Normal
mean = 0.0
standard_deviation = 1.0
[]
[]
[Likelihood]
[gaussian]
type = Gaussian
noise = 'noise_specified/noise_specified'
file_name = 'exp_0_05.csv'
log_likelihood = true
[]
[]
[Samplers]
[sample]
type = IndependentGaussianMH
prior_distributions = 'left right'
# previous_state = 'mcmc_reporter/inputs'
num_parallel_proposals = 5
file_name = 'confg.csv'
execute_on = PRE_MULTIAPP_SETUP
seed = 2547
std_prop = '0.05 0.05'
initial_values = '0.1 0.1'
seed_inputs = 'mcmc_reporter/seed_input'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Transfers]
[reporter_transfer]
type = SamplerReporterTransfer
from_reporter = 'average/value'
stochastic_reporter = 'constant'
from_multi_app = sub
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppSamplerControl
multi_app = sub
sampler = sample
param_names = 'left_bc right_bc mesh1'
[]
[]
[Reporters]
[constant]
type = StochasticReporter
[]
[noise_specified]
type = ConstantReporter
real_names = 'noise_specified'
real_values = '0.05'
[]
[mcmc_reporter]
type = IndependentMHDecision
output_value = constant/reporter_transfer:average:value
sampler = sample
likelihoods = 'gaussian'
[]
[]
[Executioner]
type = Transient
num_steps = 5
[]
[Outputs]
file_base = 'imh_5prop'
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(test/tests/times/csv_times.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Times]
[file]
type = CSVFileTimes
files = '../outputs/csv/gold/csv_transient_out.csv'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
[Outputs]
[out]
type = JSON
execute_on = FINAL
execute_system_information_on = none
[]
[]
(test/tests/meshgenerators/rename_block_generator/rename_block.i)
[Mesh]
[gmg]
type = CartesianMeshGenerator
dim = 2
dx = '1 1'
dy = '1 1'
ix = '2 2'
iy = '2 2'
subdomain_id = '0 1 2 3'
[]
[set_names]
type = RenameBlockGenerator
old_block = '0 1 2 3'
new_block = 'block0 block1 block2 block3'
input = gmg
[]
# Rename parameters supplied through the "tests" specifications
[rename]
type = RenameBlockGenerator
input = set_names
[]
# We compare by element numbers, which are not consistent in parallel
# if this is true
allow_renumbering = false
[]
[Reporters/mesh_info]
type = MeshInfo
items = subdomain_elems
[]
[Outputs/out]
type = JSON
[]
[Problem]
solve = false
[]
[Executioner]
type = Steady
[]
(test/tests/reporters/iteration_info/iteration_info.i)
[Mesh]
[generate]
type = GeneratedMeshGenerator
dim = 1
nx = 10
[]
[]
[Variables/u][]
[Kernels]
[diff]
type = ADDiffusion
variable = u
[]
[time]
type = ADTimeDerivative
variable = u
[]
[]
[BCs]
[left]
type = DirichletBC
variable = u
boundary = left
value = 0
[]
[right]
type = DirichletBC
variable = u
boundary = right
value = 10
[]
[]
[Executioner]
type = Transient
num_steps = 3
dt = 1
solve_type = NEWTON
petsc_options_iname = '-pc_type -pc_hypre_type'
petsc_options_value = 'hypre boomeramg'
[]
[Reporters/iteration_info]
type = IterationInfo
[]
[Outputs]
[out]
type = JSON
[]
[]
(test/tests/positions/functor_positions.i)
[Mesh]
[cmg]
type = CartesianMeshGenerator
dx = 1
dim = 1
[]
[]
[Positions]
[functors]
type = FunctorPositions
positions_functors = '0.1 0 0.3
f1 f2 f1'
[]
[]
[Functions]
[f1]
type = PiecewiseConstant
x = '0 0.5 1'
y = '1 2 3'
[]
[f2]
type = PiecewiseLinear
x = '0 0.5 1'
y = '1 2 3'
[]
[]
[Problem]
solve = false
[]
[Executioner]
type = Transient
# Test recover
num_steps = 2
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
[]
[]