- execute_onTIMESTEP_ENDThe list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM, ALWAYS.
Default:TIMESTEP_END
C++ Type:ExecFlagEnum
Controllable:No
Description:The list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM, ALWAYS.
- parallel_typeDISTRIBUTEDThis parameter will determine how the stochastic data is gathered. It is common for outputting purposes that this parameter be set to ROOT, otherwise, many files will be produced showing the values on each processor. However, if there are lot of samples, gathering on root may be memory restrictive.
Default:DISTRIBUTED
C++ Type:MooseEnum
Controllable:No
Description:This parameter will determine how the stochastic data is gathered. It is common for outputting purposes that this parameter be set to ROOT, otherwise, many files will be produced showing the values on each processor. However, if there are lot of samples, gathering on root may be memory restrictive.
- prop_getter_suffixAn optional suffix parameter that can be appended to any attempt to retrieve/get material properties. The suffix will be prepended with a '_' character.
C++ Type:MaterialPropertyName
Controllable:No
Description:An optional suffix parameter that can be appended to any attempt to retrieve/get material properties. The suffix will be prepended with a '_' character.
StochasticReporter
Storage container for stochastic simulation results coming from Reporters.
Description
This object is intended to be used directly with SamplerReporterTransfer, see this object for more details.
Input Parameters
- allow_duplicate_execution_on_initialFalseIn the case where this UserObject is depended upon by an initial condition, allow it to be executed twice during the initial setup (once before the IC and again after mesh adaptivity (if applicable).
Default:False
C++ Type:bool
Controllable:No
Description:In the case where this UserObject is depended upon by an initial condition, allow it to be executed twice during the initial setup (once before the IC and again after mesh adaptivity (if applicable).
- 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.
- force_postauxFalseForces the UserObject to be executed in POSTAUX
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in POSTAUX
- force_preauxFalseForces the UserObject to be executed in PREAUX
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in PREAUX
- force_preicFalseForces the UserObject to be executed in PREIC during initial setup
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in PREIC during initial setup
- outputsVector of output names were you would like to restrict the output of variables(s) associated with this object
C++ Type:std::vector<OutputName>
Controllable:No
Description:Vector of output names were you would like to restrict the output of variables(s) associated with this object
- use_displaced_meshFalseWhether or not this object should use the displaced mesh for computation. Note that in the case this is true but no displacements are provided in the Mesh block the undisplaced mesh will still be used.
Default:False
C++ Type:bool
Controllable:No
Description:Whether or not this object should use the displaced mesh for computation. Note that in the case this is true but no displacements are provided in the Mesh block the undisplaced mesh will still be used.
Advanced Parameters
Input Files
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_small.i)
- (modules/combined/examples/stochastic/poly_chaos_train_uniform.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_locs.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential_tuned.i)
- (modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_normal_quad.i)
- (modules/stochastic_tools/test/tests/surrogates/polynomial_regression/poly_reg_vec.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_mc.i)
- (modules/stochastic_tools/examples/sobol/main.i)
- (modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D_tuned.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_moment.i)
- (modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss.i)
- (modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_full_solve.i)
- (modules/stochastic_tools/test/tests/samplers/ParallelSubsetSimulation/pss_error1.i)
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_batch.i)
- (modules/stochastic_tools/test/tests/multiapps/partitioning/main_transient.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad.i)
- (modules/stochastic_tools/test/tests/transfers/sampler_reporter/main.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_main.i)
- (modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_train.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int_tuned.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int.i)
- (modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_train.i)
- (modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
- (modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_main.i)
- (modules/stochastic_tools/examples/parameter_study/main_vector.i)
- (modules/stochastic_tools/test/tests/multiapps/partitioning/main.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_normal_mc.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential.i)
- (modules/stochastic_tools/test/tests/surrogates/load_store/train_and_evaluate.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad_moment.i)
- (modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_transient.i)
- (modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D.i)
- (modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_tuned.i)
- (modules/combined/examples/stochastic/lhs_uniform.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_mc.i)
- (modules/stochastic_tools/test/tests/reporters/stochastic_reporter/stats.i)
- (modules/stochastic_tools/examples/surrogates/gaussian_process/GP_normal_mc.i)
- (modules/stochastic_tools/test/tests/surrogates/load_store/train.i)
- (modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D_tuned.i)
- (modules/stochastic_tools/test/tests/reporters/sobol/sobol_main.i)
- (modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad_locs.i)
- (modules/stochastic_tools/examples/parameter_study/main_time.i)
- (modules/stochastic_tools/examples/parameter_study/main.i)
- (modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D.i)
- (modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_quad.i)
- (modules/stochastic_tools/test/tests/samplers/AdaptiveImportanceSampler/ais.i)
- (modules/stochastic_tools/test/tests/reporters/statistics/statistics_main.i)
Child Objects
(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 = MultiAppCommandLineControl
multi_app = sub
param_names = 'BCs/left/value'
sampler = sample
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
execute_on = timestep_end
[]
[]
(modules/combined/examples/stochastic/poly_chaos_train_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
[]
[]
[GlobalParams]
distributions = 'cond_inner cond_outer heat_source alpha_inner alpha_outer ymod_inner ymod_outer prat_inner prat_outer'
[]
[Samplers]
[sample]
type = Quadrature
sparse_grid = smolyak
order = 5
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'
to_control = 'stochastic'
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
[]
[]
[Trainers]
[temp_center_inner]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:temp_center_inner:value
[]
[temp_center_outer]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:temp_center_outer:value
[]
[temp_end_inner]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:temp_end_inner:value
[]
[temp_end_outer]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:temp_end_outer:value
[]
[dispx_center_inner]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispx_center_inner:value
[]
[dispx_center_outer]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispx_center_outer:value
[]
[dispx_end_inner]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispx_end_inner:value
[]
[dispx_end_outer]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispx_end_outer:value
[]
[dispz_inner]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispz_inner:value
[]
[dispz_outer]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 4
sampler = sample
response = storage/data:dispz_outer:value
[]
[]
[Outputs]
[out]
type = SurrogateTrainerOutput
trainers = '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'
execute_on = FINAL
[]
[]
(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'
to_control = 'stochastic'
[]
[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_system_information_on = none
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2dnorm_quad.i)
[StochasticTools]
[]
[Distributions]
[D_dist]
type = Normal
mean = 5
standard_deviation = 0.5
[]
[S_dist]
type = Normal
mean = 8
standard_deviation = 0.7
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 100
distributions = 'D_dist S_dist'
execute_on = timestep_end
[]
[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'
to_control = 'stochastic'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[pc_samp]
type = EvaluateSurrogate
model = poly_chaos
sampler = sample
parallel_type = ROOT
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 = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential_tuned.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose an exponential for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
tao_options = '-tao_bncg_type ssml_bfgs'
tune_parameters = 'signal_variance length_factor'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=ExponentialCovariance
gamma = 2 #Define the exponential factor
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.551133 0.551133' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.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 = Quadrature
order = 5
distributions = 'C_dist f_dist init_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[runner]
type = SamplerFullSolveMultiApp
input_files = 'trans_diff_sub.i'
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = runner
sampler = sample
param_names = 'Materials/diff_coeff/constant_expressions Functions/src_func/vals Variables/T/initial_condition'
[]
[]
[Transfers]
[results]
type = SamplerReporterTransfer
from_multi_app = runner
sampler = sample
stochastic_reporter = trainer_results
from_reporter = 'time_max/value time_min/value'
[]
[]
[Reporters]
[trainer_results]
type = StochasticReporter
[]
[]
[Trainers]
[pc_max]
type = PolynomialChaosTrainer
execute_on = final
order = 5
distributions = 'C_dist f_dist init_dist'
sampler = sample
response = trainer_results/results:time_max:value
[]
[pc_min]
type = PolynomialChaosTrainer
execute_on = final
order = 5
distributions = 'C_dist f_dist init_dist'
sampler = sample
response = trainer_results/results:time_min:value
[]
[np_max]
type = NearestPointTrainer
execute_on = final
sampler = sample
response = trainer_results/results:time_max:value
[]
[np_min]
type = NearestPointTrainer
execute_on = final
sampler = sample
response = trainer_results/results:time_min:value
[]
[pr_max]
type = PolynomialRegressionTrainer
regression_type = "ols"
execute_on = final
max_degree = 4
sampler = sample
response = trainer_results/results:time_max:value
[]
[pr_min]
type = PolynomialRegressionTrainer
regression_type = "ols"
execute_on = final
max_degree = 4
sampler = sample
response = trainer_results/results:time_min:value
[]
[]
[Outputs]
[out]
type = SurrogateTrainerOutput
trainers = 'pc_max pc_min np_max np_min pr_max pr_min'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_quad.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 = Quadrature
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = results
from_reporter = 'avg/value max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[poly_chaos_avg]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:avg:value
[]
[poly_chaos_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:max:value
[]
[]
[Outputs]
file_base = poly_chaos_training
[out]
type = SurrogateTrainerOutput
trainers = 'poly_chaos_avg poly_chaos_max'
execute_on = FINAL
[]
[]
(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
[]
[]
[GlobalParams]
sampler = sample
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub_vector.i
mode = batch-reset
execute_on = initial
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
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/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose a squared exponential for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
[]
[]
[Covariance]
[covar]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-6 #A small amount of noise can help with numerical stability
length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
file_base = gauss_process_training
[out]
type = SurrogateTrainerOutput
trainers = 'GP_avg_trainer'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_mc.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 = 10000
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = results
from_reporter = 'avg/value max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[poly_chaos_avg]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:avg:value
[]
[poly_chaos_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:max:value
[]
[]
[Outputs]
file_base = poly_chaos_training
[out]
type = SurrogateTrainerOutput
trainers = 'poly_chaos_avg poly_chaos_max'
execute_on = FINAL
[]
[]
(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'
to_control = 'stochastic'
[]
[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
[]
[]
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D_tuned.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 50
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[cart_sample]
type = CartesianProduct
linear_space_items = '1 0.09 100
9000 20 100 '
execute_on = initial
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'rbf'
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
tao_options = '-tao_bncg_type kd'
sampler = train_sample
response = results/data:avg:value
tune_parameters = ' signal_variance length_factor'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
[]
[]
[Covariance]
[rbf]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = 'GP_avg_trainer'
[]
[]
[Reporters]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[cart_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = cart_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose a squared exponential for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-6 #A small amount of noise can help with numerical stability
length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
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'
to_control = 'stochastic'
[]
[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
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'
output_reporter = 'constant/reporter_transfer:average:value'
inputs_reporter = 'adaptive_MC/inputs'
num_samplessub = 20
use_absolute_value = true
num_parallel_chains = 2
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'
to_control = 'stochastic'
[]
[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 = 20
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_full_solve.i)
[StochasticTools]
[]
[Samplers/sample]
type = CartesianProduct
linear_space_items = '1 1 3
1 1 3'
execute_on = 'PRE_MULTIAPP_SETUP'
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
sampler = sample
input_files = 'sub_steady.i'
cli_args = 'Mesh/xmax=10;Mesh/ymax=10'
[]
[Transfers]
inactive = 'param'
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'Functions/fun/value Postprocessors/function_val/scale_factor'
to_control = receiver
[]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
from_reporter = 'size/value function_val/value'
stochastic_reporter = 'storage'
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Functions/fun/value Postprocessors/function_val/scale_factor'
[]
[]
[Reporters/storage]
type = StochasticReporter
parallel_type = ROOT
[]
[Outputs]
csv = true
execute_on = timestep_end
[]
(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'
to_control = 'stochastic'
[]
[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
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'
to_control = stm
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = none
execute_on = timestep_end
[]
[]
(modules/stochastic_tools/test/tests/multiapps/partitioning/main_transient.i)
[StochasticTools]
[]
[Samplers/sample]
type = CartesianProduct
linear_space_items = '0 1 5'
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = sample
[]
[MultiApps/sub]
type = SamplerTransientMultiApp
input_files = sub_transient.i
[]
[Controls/cli]
type = MultiAppCommandLineControl
multi_app = sub
param_names = 'Postprocessors/pp1/scale_factor'
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
to_control = receiver
parameters = 'Postprocessors/pp2/scale_factor'
[]
[rep]
type = SamplerReporterTransfer
from_multi_app = sub
stochastic_reporter = reporter
from_reporter = 'pp1/value'
[]
[pp]
type = SamplerPostprocessorTransfer
from_multi_app = sub
to_vector_postprocessor = vpp
from_postprocessor = 'pp2'
[]
[]
[VectorPostprocessors/vpp]
type = StochasticResults
[]
[Reporters]
[reporter]
type = StochasticReporter
outputs = none
[]
[check]
type = TestReporterPartitioning
sampler = sample
reporters = 'reporter/rep:pp1:value vpp/pp:pp2'
[]
[]
[Executioner]
type = Transient
num_steps = 3
[]
[Outputs]
csv = true
execute_on = timestep_end
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_quad.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]
[sample]
type = MonteCarlo
num_rows = 100
distributions = 'D_dist S_dist'
execute_on = timestep_end
[]
[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'
to_control = 'stochastic'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
[]
[pc_samp]
type = EvaluateSurrogate
model = poly_chaos
sampler = sample
parallel_type = ROOT
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 = CSV
execute_on = FINAL
[]
[]
(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 = MultiAppCommandLineControl
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
execute_system_information_on = none
[]
[]
(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 = MultiAppCommandLineControl
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
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_train.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]
[pc_sampler]
type = Quadrature
order = 8
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[pr_sampler]
type = LatinHypercube
distributions = 'k_dist q_dist L_dist Tinf_dist'
num_rows = 6560
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[pc_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = pc_sampler
[]
[pr_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = pr_sampler
[]
[]
[Controls]
[pc_cmdline]
type = MultiAppCommandLineControl
multi_app = pc_sub
sampler = pc_sampler
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[pr_cmdline]
type = MultiAppCommandLineControl
multi_app = pr_sub
sampler = pr_sampler
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[pc_data]
type = SamplerReporterTransfer
from_multi_app = pc_sub
sampler = pc_sampler
stochastic_reporter = results
from_reporter = 'max/value'
[]
[pr_data]
type = SamplerReporterTransfer
from_multi_app = pr_sub
sampler = pr_sampler
stochastic_reporter = results
from_reporter = 'max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[pc_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 8
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = pc_sampler
response = results/pc_data:max:value
[]
[pr_max]
type = PolynomialRegressionTrainer
regression_type = "ols"
execute_on = timestep_end
max_degree = 4
sampler = pr_sampler
response = results/pr_data:max:value
[]
[]
[Outputs]
[pc_out]
type = SurrogateTrainerOutput
trainers = 'pc_max'
execute_on = FINAL
[]
[pr_out]
type = SurrogateTrainerOutput
trainers = 'pr_max'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int_tuned.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose a Matern with half-integer argument for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
tao_options = '-tao_bncg_type ssml_bfgs'
tune_parameters = ' signal_variance length_factor'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=MaternHalfIntCovariance
p = 2 #Define the exponential factor
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.551133 0.551133' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose a Matern with half-integer argument for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=MaternHalfIntCovariance
p = 2 #Define the exponential factor
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-6 #A small amount of noise can help with numerical stability
length_factor = '0.551133 0.551133' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_train.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]
[pc_sampler]
type = Quadrature
order = 8
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[pr_sampler]
type = LatinHypercube
distributions = 'k_dist q_dist L_dist Tinf_dist'
num_rows = 6560
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[pc_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = pc_sampler
[]
[pr_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = pr_sampler
[]
[]
[Controls]
[pc_cmdline]
type = MultiAppCommandLineControl
multi_app = pc_sub
sampler = pc_sampler
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[pr_cmdline]
type = MultiAppCommandLineControl
multi_app = pr_sub
sampler = pr_sampler
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[pc_data]
type = SamplerReporterTransfer
from_multi_app = pc_sub
sampler = pc_sampler
stochastic_reporter = results
from_reporter = 'max/value'
[]
[pr_data]
type = SamplerReporterTransfer
from_multi_app = pr_sub
sampler = pr_sampler
stochastic_reporter = results
from_reporter = 'max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[pc_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 8
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = pc_sampler
response = results/pc_data:max:value
[]
[pr_max]
type = PolynomialRegressionTrainer
execute_on = timestep_end
regression_type = "ols"
max_degree = 4
sampler = pr_sampler
response = results/pr_data:max:value
[]
[]
[Outputs]
[pc_out]
type = SurrogateTrainerOutput
trainers = 'pc_max'
execute_on = FINAL
[]
[pr_out]
type = SurrogateTrainerOutput
trainers = 'pr_max'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
[StochasticTools]
[]
[Samplers]
[grid]
type = CartesianProduct
execute_on = PRE_MULTIAPP_SETUP
# Grid spacing:
# k: 1.45 2.35 3.25 4.15 5.05 5.95 6.85 7.75 8.65 9.55
# q: 9100 9300 9500 9700 9900 10100 10300 10500 10700 10900
# L: 0.012 0.016 0.020 0.024 0.028 0.032 0.036 0.040 0.044 0.048
# Tinf: 291 293 295 297 299 301 303 305 307 309
linear_space_items = '1.45 0.9 10
9100 200 10
0.012 0.004 10
291 2 10'
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = grid
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = grid
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = grid
stochastic_reporter = results
from_reporter = 'avg/value max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
outputs = none
[]
[]
[VectorPostprocessors]
[sampler_data]
type = SamplerData
sampler = grid
parallel_type = DISTRIBUTED
[]
[]
[Trainers]
[nearest_point_avg]
type = NearestPointTrainer
execute_on = timestep_end
sampler = grid
predictors = 'sampler_data/grid_0'
predictor_cols = '1 2 3'
response = results/data:avg:value
[]
[nearest_point_max]
type = NearestPointTrainer
execute_on = timestep_end
sampler = grid
response = results/data:max:value
[]
[]
[Outputs]
[out]
type = SurrogateTrainerOutput
trainers = 'nearest_point_avg nearest_point_max'
execute_on = FINAL
[]
[]
(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 = MultiAppCommandLineControl
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
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'
to_control = 'stochastic'
[]
[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/multiapps/partitioning/main.i)
[StochasticTools]
[]
[Samplers/sample]
type = CartesianProduct
linear_space_items = '0 1 5'
execute_on = PRE_MULTIAPP_SETUP
[]
[GlobalParams]
sampler = sample
[]
[MultiApps/sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
[]
[Controls/cli]
type = MultiAppCommandLineControl
multi_app = sub
param_names = 'Postprocessors/pp1/scale_factor'
[]
[Transfers]
[param]
type = SamplerParameterTransfer
to_multi_app = sub
to_control = receiver
parameters = 'Postprocessors/pp2/scale_factor'
[]
[rep]
type = SamplerReporterTransfer
from_multi_app = sub
stochastic_reporter = reporter
from_reporter = 'pp1/value'
[]
[pp]
type = SamplerPostprocessorTransfer
from_multi_app = sub
to_vector_postprocessor = vpp
from_postprocessor = 'pp2'
[]
[]
[VectorPostprocessors/vpp]
type = StochasticResults
[]
[Reporters]
[reporter]
type = StochasticReporter
outputs = none
[]
[check]
type = TestReporterPartitioning
sampler = sample
reporters = 'reporter/rep:pp1:value vpp/pp:pp2'
[]
[]
[Outputs]
csv = true
execute_on = timestep_end
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_mc.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 = 10000
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = results
from_reporter = 'avg/value max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[poly_chaos_avg]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:avg:value
[]
[poly_chaos_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:max:value
[]
[]
[Outputs]
file_base = poly_chaos_training
[out]
type = SurrogateTrainerOutput
trainers = 'poly_chaos_avg poly_chaos_max'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose an exponential for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=ExponentialCovariance
gamma = 1 #Define the exponential factor
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-6 #A small amount of noise can help with numerical stability
length_factor = '0.551133 0.551133' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(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'
to_control = 'stochastic'
[]
[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_system_information_on = none
execute_on = FINAL
[]
(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'
to_control = 'stochastic'
[]
[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_system_information_on = none
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_transient.i)
[StochasticTools]
[]
[Samplers/sample]
type = CartesianProduct
linear_space_items = '1 1 3
1 1 3'
execute_on = 'PRE_MULTIAPP_SETUP'
[]
[MultiApps/sub]
type = SamplerTransientMultiApp
sampler = sample
input_files = 'sub_transient.i'
cli_args = 'Mesh/xmax=10;Mesh/ymax=10'
[]
[Transfers]
inactive = 'param'
[param]
type = SamplerParameterTransfer
to_multi_app = sub
sampler = sample
parameters = 'Functions/fun/value Postprocessors/function_val/scale_factor'
to_control = receiver
[]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
from_reporter = 'size/value function_val/value'
stochastic_reporter = 'storage'
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Functions/fun/value Postprocessors/function_val/scale_factor'
[]
[]
[Reporters/storage]
type = StochasticReporter
parallel_type = ROOT
[]
[Executioner]
type = Transient
num_steps = 3
[]
[Outputs]
csv = true
execute_on = timestep_end
[]
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 50
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[cart_sample]
type = CartesianProduct
linear_space_items = '1 0.09 100
9000 20 100 '
execute_on = initial
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[cart_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = cart_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'rbf'
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
[]
[]
[Covariance]
[rbf]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-6 #A small amount of noise can help with numerical stability
length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = 'GP_avg_trainer'
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_tuned.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 1
upper_bound = 10
[]
[q_dist]
type = Uniform
lower_bound = 9000
upper_bound = 11000
[]
[]
[Samplers]
[train_sample]
type = MonteCarlo
num_rows = 10
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[test_sample]
type = MonteCarlo
num_rows = 100
distributions = 'k_dist q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
parallel_type = ROOT
[]
[samp_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = test_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = GP_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'GP_avg'
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'covar' #Choose a squared exponential for the kernel
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
tao_options = '-tao_bncg_type ssml_bfgs'
tune_parameters = ' signal_variance length_factor'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
show_tao=true
[]
[]
[Surrogates]
[GP_avg]
type = GaussianProcess
trainer = GP_avg_trainer
[]
[]
[Covariance]
[covar]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(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'
to_control = 'stochastic'
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
[]
(modules/stochastic_tools/test/tests/surrogates/poly_chaos/main_2d_mc.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]
[sample]
type = MonteCarlo
num_rows = 100
distributions = 'D_dist S_dist'
execute_on = initial
[]
[]
[MultiApps]
[quad_sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
mode = batch-restore
[]
[]
[Transfers]
[quad]
type = SamplerParameterTransfer
to_multi_app = quad_sub
sampler = sample
parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
to_control = 'stochastic'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = sample
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
outputs = none
[]
[pc_samp]
type = EvaluateSurrogate
model = poly_chaos
sampler = sample
parallel_type = ROOT
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 = sample
response = storage/data:avg:value
[]
[]
[Outputs]
[out]
type = CSV
execute_on = FINAL
[]
[]
(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 = MultiAppCommandLineControl
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
execute_system_information_on = none
[]
[]
(modules/stochastic_tools/examples/surrogates/gaussian_process/GP_normal_mc.i)
[StochasticTools]
[]
[Distributions]
[k_dist]
type = Uniform
lower_bound = 0
upper_bound = 20
[]
[q_dist]
type = Uniform
lower_bound = 7000
upper_bound = 13000
[]
[L_dist]
type = Uniform
lower_bound = 0.0
upper_bound = 0.1
[]
[Tinf_dist]
type = Uniform
lower_bound = 270
upper_bound = 330
[]
[]
[Samplers]
[sample]
type = MonteCarlo
num_rows = 500
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[GP_avg]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'rbf'
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = sample
response = results/data:avg:value
tao_options = '-tao_bncg_type gd'
tune_parameters = ' signal_variance length_factor'
tuning_min = ' 1e-9 1e-3'
tuning_max = ' 100 100'
tuning_algorithm = 'tao'
[]
[]
[Covariance]
[rbf]
type=SquaredExponentialCovariance
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
signal_variance = 1
length_factor = '0.038971 0.038971 0.038971 0.038971' #Select a length factor for each parameter (k and q)
[]
[]
[Outputs]
file_base = GP_training_normal
[out]
type = SurrogateTrainerOutput
trainers = 'GP_avg'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/test/tests/surrogates/load_store/train.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'
to_control = 'stochastic'
[]
[data]
type = SamplerReporterTransfer
from_multi_app = quad_sub
sampler = quadrature
stochastic_reporter = storage
from_reporter = avg/value
[]
[]
[Reporters]
[storage]
type = StochasticReporter
parallel_type = ROOT
[]
[]
[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 = SurrogateTrainerOutput
trainers = 'poly_chaos'
execute_on = FINAL
[]
[]
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D_tuned.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]
[train_sample]
type = MonteCarlo
num_rows = 6
distributions = 'q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[cart_sample]
type = CartesianProduct
linear_space_items = '9000 20 100'
execute_on = initial
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
covariance_function = 'rbf'
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
sampler = train_sample
response = results/data:avg:value
tao_options = '-tao_bncg_type kd'
tune_parameters = ' signal_variance length_factor'
tuning_min = ' 1e-9 1e-9'
tuning_max = ' 1e16 1e16'
tuning_algorithm = 'tao'
[]
[]
[Covariance]
[rbf]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Surrogates]
[gauss_process_avg]
type = GaussianProcess
trainer = 'GP_avg_trainer'
[]
[]
# # Computing statistics
[VectorPostprocessors]
[hyperparams]
type = GaussianProcessData
gp_name = 'gauss_process_avg'
execute_on = final
[]
[]
[Reporters]
[cart_avg]
type = EvaluateSurrogate
model = gauss_process_avg
sampler = cart_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = gauss_process_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[Outputs]
csv = true
execute_on = FINAL
[]
(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 = MultiAppCommandLineControl
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
execute_system_information_on = NONE
[]
[]
(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'
to_control = 'stochastic'
[]
[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_system_information_on = none
execute_on = FINAL
[]
[]
(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'
to_control = 'stochastic'
[]
[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
solve_type = NEWTON
num_steps = 4
dt = 0.25
[]
[Outputs]
execute_on = timestep_end
[out]
type = JSON
[]
[]
(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'
to_control = 'stochastic'
[]
[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/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D.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]
[train_sample]
type = MonteCarlo
num_rows = 6
distributions = 'q_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[cart_sample]
type = CartesianProduct
linear_space_items = '9000 20 100'
execute_on = initial
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = train_sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = train_sample
param_names = 'Kernels/source/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = train_sample
stochastic_reporter = results
from_reporter = 'avg/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[cart_avg]
type = EvaluateSurrogate
model = gauss_process_avg
sampler = cart_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[train_avg]
type = EvaluateSurrogate
model = gauss_process_avg
sampler = train_sample
evaluate_std = 'true'
parallel_type = ROOT
execute_on = final
[]
[]
[Trainers]
[GP_avg_trainer]
type = GaussianProcessTrainer
execute_on = timestep_end
sampler = train_sample
response = results/data:avg:value
covariance_function = 'rbf'
standardize_params = 'true' #Center and scale the training params
standardize_data = 'true' #Center and scale the training data
[]
[]
[Covariance]
[rbf]
type=SquaredExponentialCovariance
signal_variance = 1 #Use a signal variance of 1 in the kernel
noise_variance = 1e-3 #A small amount of noise can help with numerical stability
length_factor = '0.38971' #Select a length factor for each parameter (k and q)
[]
[]
[Surrogates]
[gauss_process_avg]
type = GaussianProcess
trainer = 'GP_avg_trainer'
[]
[]
[Outputs]
csv = true
execute_on = FINAL
[]
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_quad.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 = Quadrature
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
execute_on = PRE_MULTIAPP_SETUP
[]
[]
[MultiApps]
[sub]
type = SamplerFullSolveMultiApp
input_files = sub.i
sampler = sample
[]
[]
[Controls]
[cmdline]
type = MultiAppCommandLineControl
multi_app = sub
sampler = sample
param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
[]
[]
[Transfers]
[data]
type = SamplerReporterTransfer
from_multi_app = sub
sampler = sample
stochastic_reporter = results
from_reporter = 'avg/value max/value'
[]
[]
[Reporters]
[results]
type = StochasticReporter
[]
[]
[Trainers]
[poly_chaos_avg]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:avg:value
[]
[poly_chaos_max]
type = PolynomialChaosTrainer
execute_on = timestep_end
order = 10
distributions = 'k_dist q_dist L_dist Tinf_dist'
sampler = sample
response = results/data:max:value
[]
[]
[Outputs]
file_base = poly_chaos_training
[out]
type = SurrogateTrainerOutput
trainers = 'poly_chaos_avg poly_chaos_max'
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 = '0.15 0.15'
output_limit = 0.45
num_samples_train = 5
std_factor = 0.8
use_absolute_value = true
seed = 1012
initial_values = '-0.10329808102501603 1.2396280668056123'
inputs_reporter = 'adaptive_MC/inputs'
[]
[]
[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
[]
[adaptive_MC]
type = AdaptiveMonteCarloDecision
output_value = constant/reporter_transfer:average:value
inputs = 'inputs'
sampler = sample
[]
[]
[Executioner]
type = Transient
num_steps = 10
[]
[Outputs]
[out]
type = JSON
execute_system_information_on = NONE
[]
[]
(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 = MultiAppCommandLineControl
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
execute_system_information_on = NONE
[]
[]
(modules/stochastic_tools/include/reporters/EvaluateSurrogate.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
// MOOSE includes
#include "StochasticReporter.h"
#include "SurrogateModelInterface.h"
#include "SurrogateModel.h"
/**
* A tool for output Sampler data.
*/
class EvaluateSurrogate : public StochasticReporter, SurrogateModelInterface
{
public:
static InputParameters validParams();
EvaluateSurrogate(const InputParameters & parameters);
virtual void initialize() override {}
virtual void execute() override;
virtual void finalize() override {}
protected:
/// Sampler for evaluating surrogate model
Sampler & _sampler;
/// The data type for the response value
const MultiMooseEnum _response_types;
/// Whether or not to compute standard deviation
std::vector<bool> _doing_std;
/// Pointers to surrogate model
std::vector<const SurrogateModel *> _model;
///@{
/// Vectors containing results of sampling model
std::vector<std::vector<Real> *> _real_values;
std::vector<std::vector<std::vector<Real>> *> _vector_real_values;
std::vector<std::vector<Real> *> _real_std;
std::vector<std::vector<std::vector<Real>> *> _vector_real_std;
///@}
};