MultiAppSamplerControl

Control for modifying the command line arguments of MultiApps.

Description

Depending on the type of stochastic analysis being performed, it may be necessary to pass command line arguments to the MultiApp objects being created. For example, if a the domain is required to be altered statistically in the x-direction the Mesh/xmax parameter in the sub-application must be altered. However, this value cannot be altered from within sub-application input file via a Control object because the xmax parameter is applied to the mesh when it is created. In this case, the MultiAppSamplerControl can be used to pass custom values to the arguments of a MultiApp that are generated from Sampler and Distribution objects.

Example Use

Consider a stochastic problem that is executing 3 simulations, but those simulations require the maximum x and y coordinates of a generated mesh be varied between 5 and 10.

First, the MultiApps block is defined to execute the desired simulations.

[Distributions<<<{"href": "../../syntax/Distributions/index.html"}>>>]
  [uniform]
    type = Uniform<<<{"description": "Continuous uniform distribution.", "href": "../distributions/Uniform.html"}>>>
    lower_bound<<<{"description": "Distribution lower bound"}>>> = 5
    upper_bound<<<{"description": "Distribution upper bound"}>>> = 10
  []
[]

Second, a Uniform distribution object must be created:

[Distributions<<<{"href": "../../syntax/Distributions/index.html"}>>>]
  [uniform]
    type = Uniform<<<{"description": "Continuous uniform distribution.", "href": "../distributions/Uniform.html"}>>>
    lower_bound<<<{"description": "Distribution lower bound"}>>> = 5
    upper_bound<<<{"description": "Distribution upper bound"}>>> = 10
  []
[]

A sampling scheme must be defined for capturing values from the uniform distribution. In this example, since there are two pieces of data to be controlled ("xmax" and "ymax") the uniform distribution is sampled twice. Since this sampled data will only be used during "PRE_MULTIAPP_SETUP" execution, so the "execute_on" parameter is setup to match.

[Samplers<<<{"href": "../../syntax/Samplers/index.html"}>>>]
  [sample]
    type = MonteCarlo<<<{"description": "Monte Carlo Sampler.", "href": "../samplers/MonteCarloSampler.html"}>>>
    num_rows<<<{"description": "The number of rows per matrix to generate."}>>> = 3
    distributions<<<{"description": "The distribution names to be sampled, the number of distributions provided defines the number of columns per matrix."}>>> = 'uniform uniform'
    execute_on<<<{"description": "The list of flag(s) indicating when this object should be executed. For a description of each flag, see https://mooseframework.inl.gov/source/interfaces/SetupInterface.html."}>>> = 'PRE_MULTIAPP_SETUP'
  []
[]

Finally, the MultiAppSamplerControl is used to apply the sampled data to the desired Mesh settings.

[Controls<<<{"href": "../../syntax/Controls/index.html"}>>>]
  [cmdline]
    type = MultiAppSamplerControl<<<{"description": "Control for modifying the command line arguments of MultiApps.", "href": "MultiAppSamplerControl.html"}>>>
    multi_app<<<{"description": "The name of the MultiApp to control."}>>> = sub
    sampler<<<{"description": "The Sampler object to utilize for altering the command line options of the MultiApp."}>>> = sample
    param_names<<<{"description": "The names of the command line parameters to set via the sampled data."}>>> = 'Mesh/xmax Mesh/ymax'
  []
[]

Vector Parameter

The vector parameter can be altered statistically with MultiAppSamplerControl. To illustrate its usage, we consider an input file listed below:

[Controls<<<{"href": "../../syntax/Controls/index.html"}>>>]
  [cmdline]
    type = MultiAppSamplerControl<<<{"description": "Control for modifying the command line arguments of MultiApps.", "href": "MultiAppSamplerControl.html"}>>>
    multi_app<<<{"description": "The name of the MultiApp to control."}>>> = sub
    sampler<<<{"description": "The Sampler object to utilize for altering the command line options of the MultiApp."}>>> = sample
    param_names<<<{"description": "The names of the command line parameters to set via the sampled data."}>>> = 'Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax[3]'
  []
[]

In this input file, the param_names includes a vector parameter with 4 entries called Materials/const/prop_values and two scalar parameters called Mesh/xmax and Mesh/ymax.

Several cases exist for modifying the vector parameter:

All four entries will be altered: set param_names = Materials/const/prop_values[0,1,2,3]. The [0,1,2,3] is the global column index of the provided distributions which implies that each entry corresponds to a different distribution.
The third and fourth entry will be altered with a same distribution: set param_names = Materials/const/prop_values[0,1,2,2]. The repeated index "2" means that their values are the same and will be sampled from a same distribution.
The second entry will not be altered: set param_names = Materials/const/prop_values[0,(0.5),1,2]. In this case, the second entry will be set as 0.5 while the first, third and last entries will be altered statistically. In general, a constant value will be provided inside the parentheses bracket.

note

If [] is used, it must be provided to every parameter. By default, if the [] is not provided it is assumed that values are scalar and captured in order.

Input Parameters

multi_appThe name of the MultiApp to control.
C++ Type:MultiAppName
Controllable:No
Description:The name of the MultiApp to control.
param_namesThe names of the command line parameters to set via the sampled data.
C++ Type:std::vector<std::string>
Controllable:No
Description:The names of the command line parameters to set via the sampled data.
samplerThe Sampler object to utilize for altering the command line options of the MultiApp.
C++ Type:SamplerName
Controllable:No
Description:The Sampler object to utilize for altering the command line options of the MultiApp.

Required Parameters

depends_onThe Controls that this control relies upon (i.e. must execute before this one)
C++ Type:std::vector<std::string>
Controllable:No
Description:The Controls that this control relies upon (i.e. must execute before this one)

Optional 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:No
Description:Set the enabled status of the MooseObject.
implicitTrueDetermines whether this object is calculated using an implicit or explicit form
Default:True
C++ Type:bool
Controllable:No
Description:Determines whether this object is calculated using an implicit or explicit form

Advanced Parameters

Input Files

(modules/stochastic_tools/test/tests/multiapps/partitioning/main.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_mc.i)
(modules/stochastic_tools/test/tests/reporters/stochastic_reporter/stats.i)
(modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_train.i)
(modules/stochastic_tools/test/tests/transfers/sampler_postprocessor/cartesian_diverge.i)
(modules/stochastic_tools/test/tests/samplers/mcmc/main_imh.i)
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)
(modules/stochastic_tools/test/tests/reporters/parallel_storage/parallel_storage_main.i)
(modules/stochastic_tools/test/tests/multiapps/nested_multiapp/stm.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D.i)
(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)
(modules/stochastic_tools/test/tests/samplers/mcmc/main_des_var.i)
(modules/stochastic_tools/test/tests/surrogates/cross_validation/poly_reg_vec.i)
(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_wrong_num_params.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential.i)
(modules/stochastic_tools/test/tests/userobjects/inverse_mapping/create_mapping_main.i)
(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_multiple.i)
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_main.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/np_vec.i)
(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_wrong_num_params.i)
(modules/stochastic_tools/test/tests/multiapps/sampler_transient_multiapp/parent_transient_cmd_control.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_quad.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D_tuned.i)
(modules/stochastic_tools/test/tests/reporters/statistics/statistics_main.i)
(modules/stochastic_tools/examples/surrogates/cross_validation/all_trainers_uniform_cv.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/GP_normal_mc.i)
(modules/combined/examples/stochastic/laser_welding_dimred/train.i)
(modules/stochastic_tools/test/tests/surrogates/polynomial_regression/poly_reg_vec.i)
(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_single.i)
(modules/stochastic_tools/test/tests/surrogates/multioutput_gp/mogp_lmc.i)
(modules/stochastic_tools/test/tests/samplers/mcmc/main_des.i)
(modules/stochastic_tools/test/tests/transfers/sampler_reporter/main.i)
(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_wrong_size.i)
(modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_transient.i)
(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_wrong_multiapp_type.i)
(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/percentile/percentile_main.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_tuned_adam.i)
(modules/stochastic_tools/test/tests/reporters/sobol/sobol_main.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_mc.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_quad.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential.i)
(modules/stochastic_tools/test/tests/reporters/morris/morris_main.i)
(modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_train.i)
(modules/stochastic_tools/test/tests/surrogates/multioutput_gp/mogp_lmc_tuned.i)
(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_single.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_1D_tuned.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_Matern_half_int_tuned_adam.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/gaussian_process_uniform_2D.i)
(modules/stochastic_tools/test/tests/samplers/mcmc/main_ss.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential_tuned_adam.i)
(modules/stochastic_tools/test/tests/multiapps/user_cli_args/main_full_solve.i)
(modules/stochastic_tools/test/tests/reporters/bootstrap_statistics/bca/bca_main.i)
(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_vector.i)
(modules/combined/examples/stochastic/laser_welding_dimred/test.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)
(modules/stochastic_tools/test/tests/transfers/sampler_reporter/main_small.i)
(modules/stochastic_tools/test/tests/reporters/directperturbation/dp_main.i)
(modules/stochastic_tools/test/tests/multiapps/partitioning/main_transient.i)
(modules/stochastic_tools/test/tests/samplers/mcmc/main_base.i)

References

No citations exist within this document.

(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_vector.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
    mode = batch-reset
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = size
  []
  [prop_A]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_A
    from_postprocessor = prop_A
  []
  [prop_B]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_B
    from_postprocessor = prop_B
  []
  [prop_C]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_C
    from_postprocessor = prop_C
  []
  [prop_D]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_D
    from_postprocessor = prop_D
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
  []
  [prop_A]
    type = StochasticResults
  []
  [prop_B]
    type = StochasticResults
  []
  [prop_C]
    type = StochasticResults
  []
  [prop_D]
    type = StochasticResults
  []
  [sample_data]
    type = SamplerData
    sampler = sample
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax[3]'
  []
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

(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 = MultiAppSamplerControl
  multi_app = sub
  param_names = 'Postprocessors/pp1/scale_factor'
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    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_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 = MultiAppSamplerControl
    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
    regression_type = integration
    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
    regression_type = integration
    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/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/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 = MultiAppSamplerControl
    multi_app = pc_sub
    sampler = pc_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
  [pr_cmdline]
    type = MultiAppSamplerControl
    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/test/tests/transfers/sampler_postprocessor/cartesian_diverge.i)

[StochasticTools]
[]

[Samplers]
  [cartesian]
    type = CartesianProduct
    linear_space_items = '0 1e-6 5'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = cartesian
    # This parameter will allow the main app to continue if a solve does not converge
    ignore_solve_not_converge = true # Default: false
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = cartesian
    to_vector_postprocessor = storage
    from_postprocessor = avg
    # If this is false, the transfer will transfer NaN for sub apps that did not converge.
    # If this is true, the transfer will transfer whatever the last computed values are.
    keep_solve_fail_value = true # Default: false
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = cartesian
    param_names = 'Executioner/nl_rel_tol'
  []
[]

[Executioner]
  type = Transient
  num_steps = 2
[]

[Outputs]
  csv = true
  execute_on = 'TIMESTEP_END'
[]

(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
  []
[]

(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 = MultiAppSamplerControl
    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/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/multiapps/nested_multiapp/stm.i)

[StochasticTools]
[]

[Samplers/sample]
  type = CartesianProduct
  execute_on = PRE_MULTIAPP_SETUP
  linear_space_items = '0 1 2
                        2 1 2
                        4 1 2
                        6 1 2
                        8 1 2'
[]

[GlobalParams]
  sampler = sample
[]

[MultiApps]
  [main]
    type = SamplerFullSolveMultiApp
    input_files = main.i
  []
[]

[Controls]
  [cli]
    type = MultiAppSamplerControl
    multi_app = main
    param_names = 'BCs/left/value sub:BCs/left/value sub:subsub:BCs/left/value sub:subsub0:BCs/right/value sub:subsub1:BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = main
    stochastic_reporter = storage
    from_reporter = 'val/value receive/sub_val receive/subsub0_left_val receive/subsub1_left_val receive/subsub0_right_val receive/subsub1_right_val'
  []
[]

[Reporters/storage]
  type = StochasticReporter
  parallel_type = ROOT
[]

[Outputs]
  csv = true
  execute_on = timestep_end
  perf_graph = true
[]

(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 = MultiAppSamplerControl
    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 = GaussianProcessSurrogate
    trainer = 'GP_avg_trainer'
  []
[]

[Outputs]
  csv = true
  execute_on = FINAL
[]

(modules/stochastic_tools/test/tests/multiapps/commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

(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
  []
[]

(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
  []
[]

(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_wrong_num_params.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'initial timestep_end'
  []
[]

[MultiApps]
  [sub]
    type = FullSolveMultiApp
    positions = '0 0 0
                 1 1 1
                 2 2 2'
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax Mesh/zmax'
  []
[]

(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 = MultiAppSamplerControl
    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 = GaussianProcessSurrogate
    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/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/multiapps/batch_commandline_control/parent_multiple.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = size
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax'
  []
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_main.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]
  [hypercube]
    type = LatinHypercube
    num_rows = 2000
    distributions = 'C_dist f_dist init_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [runner]
    type = SamplerFullSolveMultiApp
    sampler = hypercube
    input_files = 'trans_diff_sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = runner
    sampler = hypercube
    param_names = 'Materials/diff_coeff/constant_expressions Functions/src_func/vals Variables/T/initial_condition'
  []
[]

[Transfers]
  [results]
    type = SamplerPostprocessorTransfer
    from_multi_app = runner
    sampler = hypercube
    to_vector_postprocessor = results
    from_postprocessor = 'time_max time_min'
  []
[]

[VectorPostprocessors]
  [results]
    type = StochasticResults
  []
[]

[Reporters]
  [stats]
    type = StatisticsReporter
    vectorpostprocessors = results
    compute = 'mean stddev'
    ci_method = 'percentile'
    ci_levels = '0.05'
  []
[]

[Outputs]
  csv = true
[]

(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/multiapps/commandline_control/parent_wrong_num_params.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax Mesh/ymax Mesh/zmax'
  []
[]

(modules/stochastic_tools/test/tests/multiapps/sampler_transient_multiapp/parent_transient_cmd_control.i)

[StochasticTools]
  auto_create_executioner = false
[]

[Distributions]
  [uniform_0]
    type = Uniform
    lower_bound = 0.1
    upper_bound = 0.3
  []
[]

[Samplers]
  [mc]
    type = MonteCarlo
    num_rows = 5
    distributions = 'uniform_0'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[Executioner]
  type = Transient
  num_steps = 5
[]

[MultiApps]
  [runner]
    type = SamplerTransientMultiApp
    sampler = mc
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = runner
    sampler = mc
    param_names = 'BCs/left/value'
  []
[]

(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 = MultiAppSamplerControl
    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 = GaussianProcessSurrogate
    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/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 = MultiAppSamplerControl
    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/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 10
                          9000 20 10 '
    execute_on = initial
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = train_sample
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    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
    sampler = train_sample
    response = results/data:avg:value
    tune_parameters = 'rbf:signal_variance rbf:length_factor'
    tuning_min = '1e-9 1e-9'
    tuning_max = '1e16  1e16'
    batch_size = 50
    num_iters = 5000
    learning_rate = 5e-3
  []
[]

[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 = GaussianProcessSurrogate
    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/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/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 = GaussianProcessSurrogate
    trainer = gp_max
  []
  [ann_surr]
    type = LibtorchANNSurrogate
    trainer = ann_max
  []
[]

[Outputs]
  [out]
    type = JSON
    execute_on = FINAL
  []
[]

(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 = MultiAppSamplerControl
    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
    tune_parameters = 'rbf:signal_variance rbf:length_factor'
    tuning_min = ' 1e-9 1e-3'
    tuning_max = ' 100  100'
    num_iters = 200
    learning_rate = 0.005
  []
[]

[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
  []
[]

[Outputs]
  file_base = GP_training_normal
  [out]
    type = SurrogateTrainerOutput
    trainers = 'GP_avg'
    execute_on = FINAL
  []
[]

(modules/combined/examples/stochastic/laser_welding_dimred/train.i)

[StochasticTools]
[]

[Distributions]
  [R_dist]
    type = Uniform
    lower_bound = 1.25E-4
    upper_bound = 1.55E-4
  []
  [power_dist]
    type = Uniform
    lower_bound = 60
    upper_bound = 74
  []
[]

[Samplers]
  [train]
    type = MonteCarlo
    num_rows = 45
    distributions = 'R_dist power_dist'
    execute_on = PRE_MULTIAPP_SETUP
    min_procs_per_row = 2
    max_procs_per_row = 2
  []
[]

[MultiApps]
  [worker]
    type = SamplerFullSolveMultiApp
    input_files = 2d.i
    sampler = train
    mode = batch-reset
    min_procs_per_app = 2
    max_procs_per_app = 2
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = worker
    sampler = train
    param_names = 'R power'
  []
[]

[Transfers]
  [solution_transfer]
    type = SerializedSolutionTransfer
    parallel_storage = parallel_storage
    from_multi_app = worker
    sampler = train
    solution_container = solution_storage
    variables = "T"
    serialize_on_root = true
  []
[]

[Reporters]
  [parallel_storage]
    type = ParallelSolutionStorage
    variables = "T"
    outputs = none
  []
  [reduced_sol]
    type = MappingReporter
    sampler = train
    parallel_storage = parallel_storage
    mapping = pod
    variables = "T"
    outputs = json
    execute_on = final
    parallel_type = ROOT
  []
  [matrix]
    type = StochasticMatrix
    sampler = train
    parallel_type = ROOT
  []
  [svd]
    type = SingularTripletReporter
    pod_mapping = pod
    variables = "T"
    execute_on = final
  []
[]

[VariableMappings]
  [pod]
    type = PODMapping
    solution_storage = parallel_storage
    variables = "T"
    num_modes_to_compute = '8'
    extra_slepc_options = "-svd_monitor_all"
  []
[]

[Trainers]
  [mogp]
    type = GaussianProcessTrainer
    execute_on = final
    covariance_function = 'lmc'
    standardize_params = 'true'
    standardize_data = 'true'
    sampler = train
    response_type = vector_real
    response = reduced_sol/T_pod
    tune_parameters = 'lmc:acoeff_0 lmc:lambda_0 covar:signal_variance covar:length_factor'
    tuning_min = '1e-9 1e-9 1e-9 1e-9'
    tuning_max = '1e16 1e16 1e16  1e16'
    num_iters = 10000
    learning_rate = 0.0005
    show_every_nth_iteration = 10
  []
[]

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 1.0
    noise_variance = 0.0
    length_factor = '0.1 0.1'
  []
  [lmc]
    type = LMC
    covariance_functions = covar
    num_outputs = 8
    num_latent_funcs = 1
  []
[]


[Outputs]
  [json]
    type = JSON
    execute_on = FINAL
    execute_system_information_on = NONE
  []
  [pod_out]
    type = MappingOutput
    mappings = pod
    execute_on = FINAL
  []
  [mogp_out]
    type = SurrogateTrainerOutput
    trainers = mogp
    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
    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/test/tests/multiapps/commandline_control/parent_single.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax'
  []
[]

(modules/stochastic_tools/test/tests/surrogates/multioutput_gp/mogp_lmc.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Normal
    mean = 15.0
    standard_deviation = 2.0
  []
  [bc_dist]
    type = Normal
    mean = 1000.0
    standard_deviation = 100.0
  []
[]

[Samplers]
  [train]
    type = LatinHypercube
    num_rows = 10
    distributions = 'k_dist bc_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
  [test]
    type = LatinHypercube
    num_rows = 5
    distributions = 'k_dist bc_dist'
    seed = 101
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    mode = batch-reset
    sampler = train
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = train
    param_names = 'Materials/conductivity/prop_values BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = train
    stochastic_reporter = results
    from_reporter = 'T_vec/T'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
    outputs = none
  []
  [eval_test]
    type = EvaluateSurrogate
    model = mogp
    response_type = vector_real
    parallel_type = ROOT
    execute_on = timestep_end
    sampler = test
    evaluate_std = true
  []
[]

[Trainers]
  [mogp_trainer]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'lmc'
    standardize_params = 'true'
    standardize_data = 'true'
    sampler = train
    response_type = vector_real
    response = results/data:T_vec:T
  []
[]

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 2.76658083
    noise_variance = 0.0
    length_factor = '3.67866381 2.63421705'
  []
  [lmc]
    type = LMC
    covariance_functions = covar
    num_outputs = 2
    num_latent_funcs = 1
  []
[]

[Surrogates]
  [mogp]
    type = GaussianProcessSurrogate
    trainer = mogp_trainer
  []
[]

[VectorPostprocessors]
  [train_params]
    type = SamplerData
    sampler = train
    execute_on = final
  []
  [test_params]
    type = SamplerData
    sampler = test
    execute_on = final
  []
  [hyperparams]
    type = GaussianProcessData
    gp_name = mogp
    execute_on = final
  []
[]

[Outputs]
  [out]
    type = JSON
    execute_on = final
    vectorpostprocessors_as_reporters = true
    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/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/multiapps/batch_commandline_control/parent_wrong_size.i)

[StochasticTools]
[]

[MultiApps]
  [sub]
    type = FullSolveMultiApp
    positions = '0 0 0
                 1 1 1'
    input_files = 'sub.i'
  []
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 10
    distributions = 'uniform'
    execute_on = 'initial timestep_end'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    arguments = 'Mesh/nx'
  []
[]

(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'
  []
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    from_reporter = 'size/value function_val/value'
    stochastic_reporter = 'storage'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    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/test/tests/multiapps/commandline_control/parent_wrong_multiapp_type.i)

[StochasticTools]
[]

[MultiApps]
  [sub]
    type = FullSolveMultiApp
    positions = '0 0 0
                 1 1 1'
    input_files = 'sub.i'
  []
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 2
    distributions = 'uniform'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/nx'
  []
[]

(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 = MultiAppSamplerControl
    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 = StochasticMatrix
    sampler = grid
    outputs = none
  []
[]

[Trainers]
  [nearest_point_avg]
    type = NearestPointTrainer
    execute_on = timestep_end
    sampler = grid
    predictors = 'results/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/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
  []
[]

(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_tuned_adam.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 = 20
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
  [test_sample]
    type = MonteCarlo
    num_rows = 100
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = train_sample
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    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
  []
  [data]
    type = SamplerData
    sampler = test_sample
    execute_on = 'initial timestep_end'
  []
[]

[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
    tune_parameters = 'covar:signal_variance covar:length_factor'
    num_iters = 1000
    batch_size = 20
    learning_rate = 0.005
  []
[]

[Surrogates]
  [GP_avg]
    type = GaussianProcessSurrogate
    trainer = GP_avg_trainer
  []
[]

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 1.0 #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 = '1.0 1.0' #Select a length factor for each parameter (k and q)
  []
[]

[Outputs]
  [out]
    type = CSV
    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 = 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
  []
[]

(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 = MultiAppSamplerControl
    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
    regression_type = integration
    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
    regression_type = integration
    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/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 = MultiAppSamplerControl
    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_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 = MultiAppSamplerControl
    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 = GaussianProcessSurrogate
    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/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
  []
[]

(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 = MultiAppSamplerControl
    multi_app = pc_sub
    sampler = pc_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
  [pr_cmdline]
    type = MultiAppSamplerControl
    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/multioutput_gp/mogp_lmc_tuned.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Normal
    mean = 15.0
    standard_deviation = 2.0
  []
  [bc_dist]
    type = Normal
    mean = 1000.0
    standard_deviation = 100.0
  []
[]

[Samplers]
  [train]
    type = LatinHypercube
    num_rows = 10
    distributions = 'k_dist bc_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
  [test]
    type = LatinHypercube
    num_rows = 5
    distributions = 'k_dist bc_dist'
    seed = 101
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    mode = batch-reset
    sampler = train
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = train
    param_names = 'Materials/conductivity/prop_values BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = train
    stochastic_reporter = results
    from_reporter = 'T_vec/T'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
    outputs = none
  []
  [eval_test]
    type = EvaluateSurrogate
    model = mogp
    response_type = vector_real
    parallel_type = ROOT
    execute_on = timestep_end
    sampler = test
    evaluate_std = true
  []
[]

[Trainers]
  [mogp_trainer]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'lmc'
    standardize_params = 'true'
    standardize_data = 'true'
    sampler = train
    response_type = vector_real
    response = results/data:T_vec:T
    tune_parameters = 'lmc:acoeff_0 lmc:lambda_0 covar:signal_variance covar:length_factor'
    tuning_min = '1e-9 1e-9 1e-9 1e-9'
    tuning_max = '1e16 1e16 1e16  1e16'
    num_iters = 1000
    batch_size = 10
    learning_rate = 0.0005
    show_every_nth_iteration = 1
  []
[]

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 2.76658083
    noise_variance = 0.0
    length_factor = '3.67866381 2.63421705'
  []
  [lmc]
    type = LMC
    covariance_functions = covar
    num_outputs = 2
    num_latent_funcs = 1
  []
[]

[Surrogates]
  [mogp]
    type = GaussianProcessSurrogate
    trainer = mogp_trainer
  []
[]

[VectorPostprocessors]
  [train_params]
    type = SamplerData
    sampler = train
    execute_on = final
  []
  [test_params]
    type = SamplerData
    sampler = test
    execute_on = final
  []
  [hyperparams]
    type = GaussianProcessData
    gp_name = mogp
    execute_on = final
  []
[]

[Outputs]
  [out]
    type = JSON
    execute_on = final
    vectorpostprocessors_as_reporters = true
    execute_system_information_on = NONE
  []
  [surr]
    type = SurrogateTrainerOutput
    execute_on = FINAL
    trainers = "mogp_trainer"
  []
[]

(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_single.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
    mode = batch-reset
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = size
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax'
  []
[]

[Outputs]
  [out]
    type = CSV
    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 = MultiAppSamplerControl
    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
    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
    sampler = train_sample
    tune_parameters = 'rbf:signal_variance rbf:length_factor'
    tuning_min = ' 1e-9 1e-9'
    tuning_max = ' 1e16  1e16'
    num_iters = 10000
    batch_size = 6
    learning_rate = 0.0005
    show_every_nth_iteration = 1
  []
[]

[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 = GaussianProcessSurrogate
    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/surrogates/gaussian_process/GP_Matern_half_int_tuned_adam.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 = 20
    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 = MultiAppSamplerControl
    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
    tune_parameters = 'covar:signal_variance covar:length_factor'
    num_iters = 1000
    batch_size = 20
    learning_rate = 0.005
  []
[]

[Surrogates]
  [GP_avg]
    type = GaussianProcessSurrogate
    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 = '1.0 1.0' #Select a length factor for each parameter (k and q)
  []
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
    file_base = 'GP_Matern_half_int_tuned_adam'
  []
[]

(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 = MultiAppSamplerControl
    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 = GaussianProcessSurrogate
    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/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
  []
[]

(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_exponential_tuned_adam.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 = 20
    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 = MultiAppSamplerControl
    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
    tune_parameters = 'covar:signal_variance covar:length_factor'
    num_iters = 1000
    batch_size = 20
    learning_rate = 0.005
  []
[]

[Surrogates]
  [GP_avg]
    type = GaussianProcessSurrogate
    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-6 #A small amount of noise can help with numerical stability
    length_factor = '1.0 1.0' #Select a length factor for each parameter (k and q)
  []
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

(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'
  []
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    from_reporter = 'size/value function_val/value'
    stochastic_reporter = 'storage'
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    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/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
  []
[]

(modules/stochastic_tools/test/tests/multiapps/batch_commandline_control/parent_vector.i)

[StochasticTools]
[]

[Distributions]
  [uniform]
    type = Uniform
    lower_bound = 5
    upper_bound = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 3
    distributions = 'uniform uniform uniform uniform'
    execute_on = 'PRE_MULTIAPP_SETUP'
  []
[]

[MultiApps]
  [sub]
    type = SamplerFullSolveMultiApp
    sampler = sample
    input_files = 'sub.i'
    mode = batch-reset
  []
[]

[Transfers]
  [data]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = storage
    from_postprocessor = size
  []
  [prop_A]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_A
    from_postprocessor = prop_A
  []
  [prop_B]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_B
    from_postprocessor = prop_B
  []
  [prop_C]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_C
    from_postprocessor = prop_C
  []
  [prop_D]
    type = SamplerPostprocessorTransfer
    from_multi_app = sub
    sampler = sample
    to_vector_postprocessor = prop_D
    from_postprocessor = prop_D
  []
[]

[VectorPostprocessors]
  [storage]
    type = StochasticResults
  []
  [prop_A]
    type = StochasticResults
  []
  [prop_B]
    type = StochasticResults
  []
  [prop_C]
    type = StochasticResults
  []
  [prop_D]
    type = StochasticResults
  []
  [sample_data]
    type = SamplerData
    sampler = sample
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = sub
    sampler = sample
    param_names = 'Mesh/xmax[0] Materials/const/prop_values[1,(1.5),2,2] Mesh/ymax[3]'
  []
[]

[Outputs]
  [out]
    type = CSV
    execute_on = FINAL
  []
[]

(modules/combined/examples/stochastic/laser_welding_dimred/test.i)

[StochasticTools]
[]

[Distributions]
  [R_dist]
    type = Uniform
    lower_bound = 1.25E-4
    upper_bound = 1.55E-4
  []
  [power_dist]
    type = Uniform
    lower_bound = 60
    upper_bound = 70
  []
[]

[Samplers]
  [test]
    type = MonteCarlo
    num_rows = 90
    distributions = 'R_dist power_dist'
    execute_on = PRE_MULTIAPP_SETUP
    min_procs_per_row = 2
    max_procs_per_row = 2
    seed=42
  []
[]

[MultiApps]
  [worker]
    type = SamplerFullSolveMultiApp
    input_files = 2d-reconst.i
    sampler = test
    mode = batch-reset
    min_procs_per_app = 2
    max_procs_per_app = 2
  []
[]

[Controls]
  [cmdline]
    type = MultiAppSamplerControl
    multi_app = worker
    sampler = test
    param_names = 'R power'
  []
[]

[Transfers]
  [results]
    type = SamplerReporterTransfer
    from_multi_app = worker
    sampler = test
    stochastic_reporter = matrix
    from_reporter = 'l2error/value'
  []
[]

[Reporters]
  [matrix]
    type = StochasticMatrix
    sampler = test
    parallel_type = ROOT
  []
[]

[Outputs]
  [json]
    type = JSON
    execute_on = FINAL
    execute_system_information_on = NONE
  []
[]

(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 = MultiAppSamplerControl
    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/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
  []
[]

(modules/stochastic_tools/test/tests/reporters/directperturbation/dp_main.i)

[StochasticTools]
[]

[Samplers]
  [directperturbation]
    type = DirectPerturbationSampler
    nominal_parameter_values = '0.1 0.2 0.3'
    relative_perturbation_intervals = '0.1 0.2 0.3'
    perturbation_method = central_difference
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [runner]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    mode = batch-reset
    sampler = directperturbation
  []
[]

[Controls]
  [param_control]
    type = MultiAppSamplerControl
    multi_app = runner
    param_names = 'x y z'
    sampler = directperturbation
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = runner
    from_reporter = 'const/f1 const/f2 const/f3 const/f_combined'
    stochastic_reporter = storage
    sampler = directperturbation
  []
[]

[Reporters]
  [storage]
    type = StochasticReporter
    outputs = NONE
  []
  [sensitivities]
    type = DirectPerturbationReporter
    reporters = 'storage/data:const:f1 storage/data:const:f2 storage/data:const:f3 storage/data:const:f_combined'
    execute_on = FINAL
    sampler = directperturbation
  []
[]

[Outputs]
  [out]
    type = JSON
    execute_on = FINAL
    execute_system_information_on = NONE
  []
[]

(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 = MultiAppSamplerControl
  multi_app = sub
  param_names = 'Postprocessors/pp1/scale_factor'
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    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/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
  []
[]

Description
Example Use
Vector Parameter
Input Parameters
Input Files
References