SurrogateTrainerOutput

Output for trained surrogate model data.

Overview

Surrogate models, once trained, can be output to a binary file for later use by using the SurrogateTrainerOutput object. All data within the model that was delcared using declareTrainerData is automatically stored in the generated file.

Example Input File Syntax

The following snippet includes a surrogate model that is being trained and after the training is complete the model data is output using the SurrogateTrainerOutput object.

[Trainers]
  [poly_chaos]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 5
    distributions = 'D_dist S_dist'
    sampler = quadrature
    response = storage/data:avg:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos'
    execute_on = FINAL
  []
[]

Input Parameters

file_baseThe desired solution output name without an extension. If not provided, MOOSE sets it with Outputs/file_base when available. Otherwise, MOOSE uses input file name and this object name for a master input or uses master file_base, the subapp name and this object name for a subapp input to set it.
C++ Type:std::string
Controllable:No
Description:The desired solution output name without an extension. If not provided, MOOSE sets it with Outputs/file_base when available. Otherwise, MOOSE uses input file name and this object name for a master input or uses master file_base, the subapp name and this object name for a subapp input to set it.
trainersA list of SurrogateTrainer objects to output.
C++ Type:std::vector<UserObjectName>
Controllable:No
Description:A list of SurrogateTrainer objects to output.

Required Parameters

additional_execute_onThe list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS.
C++ Type:ExecFlagEnum
Options:NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS
Controllable:No
Description:The list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS.
append_dateFalseWhen true the date and time are appended to the output filename.
Default:False
C++ Type:bool
Controllable:No
Description:When true the date and time are appended to the output filename.
append_date_formatThe format of the date/time to append, if not given UTC format is used (see http://www.cplusplus.com/reference/ctime/strftime).
C++ Type:std::string
Controllable:No
Description:The format of the date/time to append, if not given UTC format is used (see http://www.cplusplus.com/reference/ctime/strftime).
end_stepTime step at which this output object stop operating
C++ Type:int
Controllable:No
Description:Time step at which this output object stop operating
execute_onINITIAL TIMESTEP_ENDThe list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS.
Default:INITIAL TIMESTEP_END
C++ Type:ExecFlagEnum
Options:NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS
Controllable:No
Description:The list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, FAILED, CUSTOM, ALWAYS.
output_linearFalseSpecifies whether output occurs on each linear residual evaluation
Default:False
C++ Type:bool
Controllable:No
Description:Specifies whether output occurs on each linear residual evaluation
output_nonlinearFalseSpecifies whether output occurs on each nonlinear residual evaluation
Default:False
C++ Type:bool
Controllable:No
Description:Specifies whether output occurs on each nonlinear residual evaluation
start_stepTime step at which this output object begins to operate
C++ Type:int
Controllable:No
Description:Time step at which this output object begins to operate
use_displacedFalseEnable/disable the use of the displaced mesh for outputting
Default:False
C++ Type:bool
Controllable:No
Description:Enable/disable the use of the displaced mesh for outputting

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:Yes
Description:Set the enabled status of the MooseObject.
output_if_base_containsIf this is supplied then output will only be done in the case that the output base contains one of these strings. This is helpful in outputting only a subset of outputs when using MultiApps.
C++ Type:std::vector<std::string>
Controllable:No
Description:If this is supplied then output will only be done in the case that the output base contains one of these strings. This is helpful in outputting only a subset of outputs when using MultiApps.
padding4The number of digits for the extension suffix (e.g., out.e-s002)
Default:4
C++ Type:unsigned int
Controllable:No
Description:The number of digits for the extension suffix (e.g., out.e-s002)

Advanced Parameters

end_timeTime at which this output object stop operating
C++ Type:double
Controllable:No
Description:Time at which this output object stop operating
interval1The interval at which time steps are output to the solution file
Default:1
C++ Type:unsigned int
Controllable:No
Description:The interval at which time steps are output to the solution file
start_timeTime at which this output object begins to operate
C++ Type:double
Controllable:No
Description:Time at which this output object begins to operate
sync_onlyFalseOnly export results at sync times
Default:False
C++ Type:bool
Controllable:No
Description:Only export results at sync times
sync_timesTimes at which the output and solution is forced to occur
C++ Type:std::vector<double>
Controllable:No
Description:Times at which the output and solution is forced to occur
time_tolerance1e-14Time tolerance utilized checking start and end times
Default:1e-14
C++ Type:double
Controllable:No
Description:Time tolerance utilized checking start and end times

Timing Parameters

linear_residual_dt_divisor1000Number of divisions applied to time step when outputting linear residuals
Default:1000
C++ Type:double
Controllable:No
Description:Number of divisions applied to time step when outputting linear residuals
linear_residual_end_timeSpecifies an end time to begin output on each linear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies an end time to begin output on each linear residual evaluation
linear_residual_start_timeSpecifies a start time to begin output on each linear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies a start time to begin output on each linear residual evaluation
nonlinear_residual_dt_divisor1000Number of divisions applied to time step when outputting non-linear residuals
Default:1000
C++ Type:double
Controllable:No
Description:Number of divisions applied to time step when outputting non-linear residuals
nonlinear_residual_end_timeSpecifies an end time to begin output on each nonlinear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies an end time to begin output on each nonlinear residual evaluation
nonlinear_residual_start_timeSpecifies a start time to begin output on each nonlinear residual evaluation
C++ Type:double
Controllable:No
Description:Specifies a start time to begin output on each nonlinear residual evaluation

Petsc Parameters

Input Files

(modules/combined/examples/stochastic/poly_chaos_train_uniform.i)
(modules/stochastic_tools/examples/surrogates/pod_rb/2d_multireg/trainer.i)
(modules/stochastic_tools/test/tests/surrogates/libtorch_nn/train.i)
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_quad.i)
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_mc.i)
(modules/stochastic_tools/test/tests/surrogates/pod_rb/errors/trainer.i)
(modules/stochastic_tools/test/tests/surrogates/pod_rb/internal/trainer.i)
(modules/stochastic_tools/examples/surrogates/polynomial_regression/uniform_train.i)
(modules/stochastic_tools/test/tests/surrogates/pod_rb/boundary/trainer.i)
(modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_train.i)
(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
(modules/stochastic_tools/test/tests/surrogates/polynomial_regression/train.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_mc.i)
(modules/stochastic_tools/examples/surrogates/gaussian_process/GP_normal_mc.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/train.i)
(modules/stochastic_tools/test/tests/surrogates/load_store/train.i)
(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_quad.i)

(modules/stochastic_tools/test/tests/surrogates/load_store/train.i)

[StochasticTools]
[]

[Distributions]
  [D_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [S_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
[]

[Samplers]
  [quadrature]
    type = Quadrature
    distributions = 'D_dist S_dist'
    execute_on = INITIAL
    order = 5
  []
[]

[MultiApps]
  [quad_sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = quadrature
    mode = batch-restore
  []
[]

[Transfers]
  [quad]
    type = SamplerParameterTransfer
    to_multi_app = quad_sub
    sampler = quadrature
    parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
    to_control = 'stochastic'
  []
  [data]
    type = SamplerReporterTransfer
    from_multi_app = quad_sub
    sampler = quadrature
    stochastic_reporter = storage
    from_reporter = avg/value
  []
[]

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

[Trainers]
  [poly_chaos]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 5
    distributions = 'D_dist S_dist'
    sampler = quadrature
    response = storage/data:avg:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos'
    execute_on = FINAL
  []
[]

(modules/combined/examples/stochastic/poly_chaos_train_uniform.i)

[StochasticTools]
[]

[Distributions]
  [cond_inner]
    type = Uniform
    lower_bound = 20
    upper_bound = 30
  []
  [cond_outer]
    type = Uniform
    lower_bound = 90
    upper_bound = 110
  []
  [heat_source]
    type = Uniform
    lower_bound = 9000
    upper_bound = 11000
  []
  [alpha_inner]
    type = Uniform
    lower_bound = 1e-6
    upper_bound = 3e-6
  []
  [alpha_outer]
    type = Uniform
    lower_bound = 5e-7
    upper_bound = 1.5e-6
  []
  [ymod_inner]
    type = Uniform
    lower_bound = 2e5
    upper_bound = 2.2e5
  []
  [ymod_outer]
    type = Uniform
    lower_bound = 3e5
    upper_bound = 3.2e5
  []
  [prat_inner]
    type = Uniform
    lower_bound = 0.29
    upper_bound = 0.31
  []
  [prat_outer]
    type = Uniform
    lower_bound = 0.19
    upper_bound = 0.21
  []
[]

[GlobalParams]
  distributions = 'cond_inner cond_outer heat_source alpha_inner alpha_outer ymod_inner ymod_outer prat_inner prat_outer'
[]

[Samplers]
  [sample]
    type = Quadrature
    sparse_grid = smolyak
    order = 5
    execute_on = INITIAL
  []
[]

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

[Transfers]
  [sub]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'Materials/cond_inner/prop_values Materials/cond_outer/prop_values
                  Postprocessors/heat_source/scale_factor
                  Materials/thermal_strain_inner/thermal_expansion_coeff Materials/thermal_strain_outer/thermal_expansion_coeff
                  Materials/elasticity_tensor_inner/youngs_modulus Materials/elasticity_tensor_outer/youngs_modulus
                  Materials/elasticity_tensor_inner/poissons_ratio Materials/elasticity_tensor_outer/poissons_ratio'
    to_control = 'stochastic'
    check_multiapp_execute_on = false
  []
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = storage
    from_reporter = 'temp_center_inner/value  temp_center_outer/value  temp_end_inner/value  temp_end_outer/value
                     dispx_center_inner/value dispx_center_outer/value dispx_end_inner/value dispx_end_outer/value
                     dispz_inner/value dispz_outer/value'
  []
[]

[Reporters]
  [storage]
    type = StochasticReporter
  []
[]

[Trainers]
  [temp_center_inner]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:temp_center_inner:value
  []
  [temp_center_outer]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:temp_center_outer:value
  []
  [temp_end_inner]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:temp_end_inner:value
  []
  [temp_end_outer]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:temp_end_outer:value
  []
  [dispx_center_inner]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispx_center_inner:value
  []
  [dispx_center_outer]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispx_center_outer:value
  []
  [dispx_end_inner]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispx_end_inner:value
  []
  [dispx_end_outer]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispx_end_outer:value
  []
  [dispz_inner]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispz_inner:value
  []
  [dispz_outer]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 4
    sampler = sample
    response = storage/data:dispz_outer:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'temp_center_inner  temp_center_outer  temp_end_inner  temp_end_outer
                dispx_center_inner dispx_center_outer dispx_end_inner dispx_end_outer
                dispz_inner dispz_outer'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/pod_rb/2d_multireg/trainer.i)

[StochasticTools]
[]

[Distributions]
  [D012_dist]
    type = Uniform
    lower_bound = 0.2
    upper_bound = 0.8
  []
  [D1_dist]
    type = Uniform
    lower_bound = 0.2
    upper_bound = 0.8
  []
  [D2_dist]
    type = Uniform
    lower_bound = 0.2
    upper_bound = 0.8
  []
  [D3_dist]
    type = Uniform
    lower_bound = 0.15
    upper_bound = 0.6
  []
  [absxs0_dist]
    type = Uniform
    lower_bound = 0.0425
    upper_bound = 0.17
  []
  [absxs1_dist]
    type = Uniform
    lower_bound = 0.065
    upper_bound = 0.26
  []
  [absxs2_dist]
    type = Uniform
    lower_bound = 0.04
    upper_bound = 0.16
  []
  [absxs3_dist]
    type = Uniform
    lower_bound = 0.005
    upper_bound = 0.02
  []
  [src_dist]
    type = Uniform
    lower_bound = 5
    upper_bound = 20
  []
[]

[Samplers]
  [sample]
    type = LatinHypercube
    distributions = 'D012_dist D012_dist D012_dist D3_dist
                     absxs0_dist absxs1_dist absxs2_dist absxs3_dist
                     src_dist src_dist src_dist'
    num_rows = 100
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = PODFullSolveMultiApp
    input_files = sub.i
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin final'
  []
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'Materials/D0/prop_values
                  Materials/D1/prop_values
                  Materials/D2/prop_values
                  Materials/D3/prop_values
                  Materials/absxs0/prop_values
                  Materials/absxs1/prop_values
                  Materials/absxs2/prop_values
                  Materials/absxs3/prop_values
                  Kernels/src0/value
                  Kernels/src1/value
                  Kernels/src2/value'
    to_control = 'stochastic'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [data]
    type = PODSamplerSolutionTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [mode]
    type = PODSamplerSolutionTransfer
    to_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
  [res]
    type = PODResidualTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
[]

[Trainers]
  [pod_rb]
    type = PODReducedBasisTrainer
    var_names = 'psi'
    error_res = '1e-9'
    tag_names = 'diff0 diff1 diff2 diff3 abs0 abs1 abs2 abs3 src0 src1 src2'
    tag_types = 'op op op op op op op op src src src'
    execute_on = 'timestep_begin final'
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'pod_rb'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/libtorch_nn/train.i)

[StochasticTools]
[]

[Samplers]
  [sample]
    type = CartesianProduct
    linear_space_items = '0 0.0125 5
                          0 0.0125 5
                          0 0.0125 5'
  []
[]

[VectorPostprocessors]
  [values]
    type = GFunction
    sampler = sample
    q_vector = '0 0 0'
    execute_on = INITIAL
    outputs = none
  []
[]

[Trainers]
  [train]
    type = LibtorchANNTrainer
    sampler = sample
    response = values/g_values
    num_epochs = 40
    num_batches = 10
    num_neurons_per_layer = '64 32'
    learning_rate = 0.001
    filename = mynet.pt
    read_from_file = false
    print_epoch_loss = 10
    activation_function = 'relu relu'
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'train'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)

[StochasticTools]
[]

[Distributions]
  [C_dist]
    type = Uniform
    lower_bound = 0.01
    upper_bound = 0.02
  []
  [f_dist]
    type = Uniform
    lower_bound = 15
    upper_bound = 25
  []
  [init_dist]
    type = Uniform
    lower_bound = 270
    upper_bound = 330
  []
[]

[Samplers]
  [sample]
    type = Quadrature
    order = 5
    distributions = 'C_dist f_dist init_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

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

[Transfers]
  [results]
    type = SamplerReporterTransfer
    from_multi_app = runner
    sampler = sample
    stochastic_reporter = trainer_results
    from_reporter = 'time_max/value time_min/value'
  []
[]

[Reporters]
  [trainer_results]
    type = StochasticReporter
  []
[]

[Trainers]
  [pc_max]
    type = PolynomialChaosTrainer
    execute_on = final
    order = 5
    distributions = 'C_dist f_dist init_dist'
    sampler = sample
    response = trainer_results/results:time_max:value
  []
  [pc_min]
    type = PolynomialChaosTrainer
    execute_on = final
    order = 5
    distributions = 'C_dist f_dist init_dist'
    sampler = sample
    response = trainer_results/results:time_min:value
  []
  [np_max]
    type = NearestPointTrainer
    execute_on = final
    sampler = sample
    response = trainer_results/results:time_max:value
  []
  [np_min]
    type = NearestPointTrainer
    execute_on = final
    sampler = sample
    response = trainer_results/results:time_min:value
  []
  [pr_max]
    type = PolynomialRegressionTrainer
    regression_type = "ols"
    execute_on = final
    max_degree = 4
    sampler = sample
    response = trainer_results/results:time_max:value
  []
  [pr_min]
    type = PolynomialRegressionTrainer
    regression_type = "ols"
    execute_on = final
    max_degree = 4
    sampler = sample
    response = trainer_results/results:time_min:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'pc_max pc_min np_max np_min pr_max pr_min'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_quad.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Normal
    mean = 5
    standard_deviation = 2
  []
  [q_dist]
    type = Normal
    mean = 10000
    standard_deviation = 500
  []
  [L_dist]
    type = Normal
    mean = 0.03
    standard_deviation = 0.01
  []
  [Tinf_dist]
    type = Normal
    mean = 300
    standard_deviation = 10
  []
[]

[Samplers]
  [sample]
    type = Quadrature
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = results
    from_reporter = 'avg/value max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [poly_chaos_avg]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:avg:value
  []
  [poly_chaos_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:max:value
  []
[]

[Outputs]
  file_base = poly_chaos_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos_avg poly_chaos_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 1
    upper_bound = 10
  []
  [q_dist]
    type = Uniform
    lower_bound = 9000
    upper_bound = 11000
  []
[]

[Samplers]
  [train_sample]
    type = MonteCarlo
    num_rows = 10
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = train_sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = train_sample
    stochastic_reporter = results
    from_reporter = 'avg/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
    parallel_type = ROOT
  []
[]

[Trainers]
  [GP_avg_trainer]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'covar'             #Choose a squared exponential for the kernel
    standardize_params = 'true'               #Center and scale the training params
    standardize_data = 'true'                 #Center and scale the training data
    sampler = train_sample
    response = results/data:avg:value
  []
[]

[Covariance]
  [covar]
    type=SquaredExponentialCovariance
    signal_variance = 1                       #Use a signal variance of 1 in the kernel
    noise_variance = 1e-6                     #A small amount of noise can help with numerical stability
    length_factor = '0.38971 0.38971'         #Select a length factor for each parameter (k and q)
  []
[]

[Outputs]
  file_base = gauss_process_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'GP_avg_trainer'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_mc.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 1
    upper_bound = 10
  []
  [q_dist]
    type = Uniform
    lower_bound = 9000
    upper_bound = 11000
  []
  [L_dist]
    type = Uniform
    lower_bound = 0.01
    upper_bound = 0.05
  []
  [Tinf_dist]
    type = Uniform
    lower_bound = 290
    upper_bound = 310
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 10000
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = results
    from_reporter = 'avg/value max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [poly_chaos_avg]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:avg:value
  []
  [poly_chaos_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:max:value
  []
[]

[Outputs]
  file_base = poly_chaos_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos_avg poly_chaos_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/pod_rb/errors/trainer.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [alpha_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [S_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
[]

[Samplers]
  [sample]
    type = LatinHypercube
    distributions = 'k_dist alpha_dist S_dist'
    num_rows = 3
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = PODFullSolveMultiApp
    input_files = sub.i
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin final'
  []
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'Materials/k/prop_values Materials/alpha/prop_values Kernels/source/value'
    to_control = 'stochastic'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [snapshot]
    type = PODSamplerSolutionTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [mode]
    type = PODSamplerSolutionTransfer
    to_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
  [res]
    type = PODResidualTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = "pod_rb"
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
[]

[Trainers]
  [pod_rb]
    type = PODReducedBasisTrainer
    var_names = 'u'
    error_res = '1e-9'
    tag_names = 'diff react bodyf'
    tag_types = 'op op src'
    execute_on = 'timestep_begin final'
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'pod_rb'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/pod_rb/internal/trainer.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [alpha_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [S_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
[]

[Samplers]
  [sample]
    type = LatinHypercube
    distributions = 'k_dist alpha_dist S_dist'
    num_rows = 3
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = PODFullSolveMultiApp
    input_files = sub.i
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin final'
  []
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'Materials/k/prop_values Materials/alpha/prop_values Kernels/source/value'
    to_control = 'stochastic'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [snapshots]
    type = PODSamplerSolutionTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [pod_modes]
    type = PODSamplerSolutionTransfer
    to_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
  [res]
    type = PODResidualTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = "pod_rb"
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
[]

[Trainers]
  [pod_rb]
    type = PODReducedBasisTrainer
    var_names = 'u'
    error_res = '1e-9'
    tag_names = 'diff react bodyf'
    tag_types = 'op op src'
    execute_on = 'timestep_begin final'
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'pod_rb'
    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 = MultiAppCommandLineControl
    multi_app = pc_sub
    sampler = pc_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
  [pr_cmdline]
    type = MultiAppCommandLineControl
    multi_app = pr_sub
    sampler = pr_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [pc_data]
    type = SamplerReporterTransfer
    from_multi_app = pc_sub
    sampler = pc_sampler
    stochastic_reporter = results
    from_reporter = 'max/value'
  []
  [pr_data]
    type = SamplerReporterTransfer
    from_multi_app = pr_sub
    sampler = pr_sampler
    stochastic_reporter = results
    from_reporter = 'max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [pc_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 8
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = pc_sampler
    response = results/pc_data:max:value
  []
  [pr_max]
    type = PolynomialRegressionTrainer
    regression_type = "ols"
    execute_on = timestep_end
    max_degree = 4
    sampler = pr_sampler
    response = results/pr_data:max:value
  []
[]

[Outputs]
  [pc_out]
    type = SurrogateTrainerOutput
    trainers = 'pc_max'
    execute_on = FINAL
  []
  [pr_out]
    type = SurrogateTrainerOutput
    trainers = 'pr_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/pod_rb/boundary/trainer.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [alpha_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [S_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [Dir_dist]
    type = Uniform
    lower_bound = 0
    upper_bound = 1
  []
[]

[Samplers]
  [sample]
    type = LatinHypercube
    distributions = 'k_dist alpha_dist S_dist Dir_dist'
    num_rows = 5
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [sub]
    type = PODFullSolveMultiApp
    input_files = sub.i
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin final'
  []
[]

[Transfers]
  [param]
    type = SamplerParameterTransfer
    to_multi_app = sub
    sampler = sample
    parameters = 'Materials/k/prop_values Materials/alpha/prop_values Kernels/source/value BCs/left/value'
    to_control = 'stochastic'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [data]
    type = PODSamplerSolutionTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'timestep_begin'
    check_multiapp_execute_on = false
  []
  [mode]
    type = PODSamplerSolutionTransfer
    to_multi_app = sub
    sampler = sample
    trainer_name = 'pod_rb'
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
  [res]
    type = PODResidualTransfer
    from_multi_app = sub
    sampler = sample
    trainer_name = "pod_rb"
    execute_on = 'final'
    check_multiapp_execute_on = false
  []
[]

[Trainers]
  [pod_rb]
    type = PODReducedBasisTrainer
    var_names = 'u'
    error_res = '1e-9'
    tag_names = 'diff react bodyf dir_src dir_imp'
    tag_types = 'op op src src_dir op_dir'
    execute_on = 'timestep_begin final'
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'pod_rb'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/polynomial_regression/normal_train.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Normal
    mean = 5
    standard_deviation = 2
  []
  [q_dist]
    type = Normal
    mean = 10000
    standard_deviation = 500
  []
  [L_dist]
    type = Normal
    mean = 0.03
    standard_deviation = 0.01
  []
  [Tinf_dist]
    type = Normal
    mean = 300
    standard_deviation = 10
  []
[]

[Samplers]
  [pc_sampler]
    type = Quadrature
    order = 8
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
  [pr_sampler]
    type = LatinHypercube
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    num_rows = 6560
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

[MultiApps]
  [pc_sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = pc_sampler
  []
  [pr_sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = pr_sampler
  []
[]

[Controls]
  [pc_cmdline]
    type = MultiAppCommandLineControl
    multi_app = pc_sub
    sampler = pc_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
  [pr_cmdline]
    type = MultiAppCommandLineControl
    multi_app = pr_sub
    sampler = pr_sampler
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [pc_data]
    type = SamplerReporterTransfer
    from_multi_app = pc_sub
    sampler = pc_sampler
    stochastic_reporter = results
    from_reporter = 'max/value'
  []
  [pr_data]
    type = SamplerReporterTransfer
    from_multi_app = pr_sub
    sampler = pr_sampler
    stochastic_reporter = results
    from_reporter = 'max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [pc_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 8
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = pc_sampler
    response = results/pc_data:max:value
  []
  [pr_max]
    type = PolynomialRegressionTrainer
    execute_on = timestep_end
    regression_type = "ols"
    max_degree = 4
    sampler = pr_sampler
    response = results/pr_data:max:value
    []
[]

[Outputs]
  [pc_out]
    type = SurrogateTrainerOutput
    trainers = 'pc_max'
    execute_on = FINAL
  []
  [pr_out]
    type = SurrogateTrainerOutput
    trainers = 'pr_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)

[StochasticTools]
[]

[Samplers]
  [grid]
    type = CartesianProduct
    execute_on = PRE_MULTIAPP_SETUP
    # Grid spacing:
    # k:    1.45  2.35  3.25  4.15  5.05  5.95  6.85  7.75  8.65  9.55
    # q:    9100  9300  9500  9700  9900  10100 10300 10500 10700 10900
    # L:    0.012 0.016 0.020 0.024 0.028 0.032 0.036 0.040 0.044 0.048
    # Tinf: 291   293   295   297   299   301   303   305   307   309
    linear_space_items = '1.45  0.9   10
                          9100  200   10
                          0.012 0.004 10
                          291   2     10'
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = grid
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = grid
    stochastic_reporter = results
    from_reporter = 'avg/value max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
    outputs = none
  []
[]

[VectorPostprocessors]
  [sampler_data]
    type = SamplerData
    sampler = grid
    parallel_type = DISTRIBUTED
  []
[]

[Trainers]
  [nearest_point_avg]
    type = NearestPointTrainer
    execute_on = timestep_end
    sampler = grid
    predictors = 'sampler_data/grid_0'
    predictor_cols = '1 2 3'
    response = results/data:avg:value
  []
  [nearest_point_max]
    type = NearestPointTrainer
    execute_on = timestep_end
    sampler = grid
    response = results/data:max:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'nearest_point_avg nearest_point_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/polynomial_regression/train.i)

[StochasticTools]
[]

[Samplers]
  [sample]
    type = CartesianProduct
    linear_space_items = '0 1 10
                          0 1 10
                          0 1 10'
  []
[]

[VectorPostprocessors]
  [values]
    type = GFunction
    sampler = sample
    q_vector = '0 0 0'
    execute_on = INITIAL
    outputs = none
  []
[]

[Trainers]
  [train]
    type = PolynomialRegressionTrainer
    regression_type = "ols"
    sampler = sample
    response = values/g_values
    max_degree = 3
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'train'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/poly_chaos_normal_mc.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Normal
    mean = 5
    standard_deviation = 2
  []
  [q_dist]
    type = Normal
    mean = 10000
    standard_deviation = 500
  []
  [L_dist]
    type = Normal
    mean = 0.03
    standard_deviation = 0.01
  []
  [Tinf_dist]
    type = Normal
    mean = 300
    standard_deviation = 10
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 10000
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = results
    from_reporter = 'avg/value max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [poly_chaos_avg]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:avg:value
  []
  [poly_chaos_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:max:value
  []
[]

[Outputs]
  file_base = poly_chaos_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos_avg poly_chaos_max'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/gaussian_process/GP_normal_mc.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 0
    upper_bound = 20
  []
  [q_dist]
    type = Uniform
    lower_bound = 7000
    upper_bound = 13000
  []
  [L_dist]
    type = Uniform
    lower_bound = 0.0
    upper_bound = 0.1
  []
  [Tinf_dist]
    type = Uniform
    lower_bound = 270
    upper_bound = 330
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 500
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = results
    from_reporter = 'avg/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [GP_avg]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'rbf'
    standardize_params = 'true'               #Center and scale the training params
    standardize_data = 'true'                 #Center and scale the training data
    sampler = sample
    response = results/data:avg:value
    tao_options = '-tao_bncg_type gd'
    tune_parameters = ' signal_variance length_factor'
    tuning_min = ' 1e-9 1e-3'
    tuning_max = ' 100  100'
    tuning_algorithm = 'tao'
  []
[]

[Covariance]
  [rbf]
    type=SquaredExponentialCovariance
    noise_variance = 1e-3                     #A small amount of noise can help with numerical stability
    signal_variance = 1
    length_factor = '0.038971 0.038971 0.038971 0.038971' #Select a length factor for each parameter (k and q)
  []
[]

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

(modules/stochastic_tools/test/tests/surrogates/nearest_point/train.i)

[StochasticTools]
[]

[Samplers]
  [sample]
    type = CartesianProduct
    linear_space_items = '0 1 10
                          0 1 10
                          0 1 10'
  []
[]

[VectorPostprocessors]
  [values]
    type = GFunction
    sampler = sample
    q_vector = '0 0 0'
    execute_on = INITIAL
    outputs = none
  []
[]

[Trainers]
  [train]
    type = NearestPointTrainer
    sampler = sample
    response = values/g_values
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'train'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/test/tests/surrogates/load_store/train.i)

[StochasticTools]
[]

[Distributions]
  [D_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
  [S_dist]
    type = Uniform
    lower_bound = 2.5
    upper_bound = 7.5
  []
[]

[Samplers]
  [quadrature]
    type = Quadrature
    distributions = 'D_dist S_dist'
    execute_on = INITIAL
    order = 5
  []
[]

[MultiApps]
  [quad_sub]
    type = SamplerFullSolveMultiApp
    input_files = sub.i
    sampler = quadrature
    mode = batch-restore
  []
[]

[Transfers]
  [quad]
    type = SamplerParameterTransfer
    to_multi_app = quad_sub
    sampler = quadrature
    parameters = 'Materials/diffusivity/prop_values Materials/xs/prop_values'
    to_control = 'stochastic'
  []
  [data]
    type = SamplerReporterTransfer
    from_multi_app = quad_sub
    sampler = quadrature
    stochastic_reporter = storage
    from_reporter = avg/value
  []
[]

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

[Trainers]
  [poly_chaos]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 5
    distributions = 'D_dist S_dist'
    sampler = quadrature
    response = storage/data:avg:value
  []
[]

[Outputs]
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos'
    execute_on = FINAL
  []
[]

(modules/stochastic_tools/examples/surrogates/poly_chaos_uniform_quad.i)

[StochasticTools]
[]

[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 1
    upper_bound = 10
  []
  [q_dist]
    type = Uniform
    lower_bound = 9000
    upper_bound = 11000
  []
  [L_dist]
    type = Uniform
    lower_bound = 0.01
    upper_bound = 0.05
  []
  [Tinf_dist]
    type = Uniform
    lower_bound = 290
    upper_bound = 310
  []
[]

[Samplers]
  [sample]
    type = Quadrature
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    execute_on = PRE_MULTIAPP_SETUP
  []
[]

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

[Controls]
  [cmdline]
    type = MultiAppCommandLineControl
    multi_app = sub
    sampler = sample
    param_names = 'Materials/conductivity/prop_values Kernels/source/value Mesh/xmax BCs/right/value'
  []
[]

[Transfers]
  [data]
    type = SamplerReporterTransfer
    from_multi_app = sub
    sampler = sample
    stochastic_reporter = results
    from_reporter = 'avg/value max/value'
  []
[]

[Reporters]
  [results]
    type = StochasticReporter
  []
[]

[Trainers]
  [poly_chaos_avg]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:avg:value
  []
  [poly_chaos_max]
    type = PolynomialChaosTrainer
    execute_on = timestep_end
    order = 10
    distributions = 'k_dist q_dist L_dist Tinf_dist'
    sampler = sample
    response = results/data:max:value
  []
[]

[Outputs]
  file_base = poly_chaos_training
  [out]
    type = SurrogateTrainerOutput
    trainers = 'poly_chaos_avg poly_chaos_max'
    execute_on = FINAL
  []
[]

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