NearestPointTrainer

Loops over and saves sample values for NearestPointSurrogate.

Overview

This trainer is meant to produce training data to be used with NearestPointSurrogate. The data saved (_sample_points) is an array of the sample points and the results from the full model:

var element = document.getElementById("moose-equation-fbabd034-255b-4e4f-8f93-3e55ca5ca712");katex.render("\\texttt{\\_sample\\_points} = \\begin{bmatrix} x_{1,1} & x_{1,2} & \\dots & x_{1,N} \\\\ x_{2,1} & x_{2,2} & \\dots & x_{2,N} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ x_{D,1} & x_{D,2} & \\dots & x_{D,N} \\\\ y_1 & y_2 & \\dots & y_N \\end{bmatrix}", element, {displayMode:true,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});

where $var element = document.getElementById("moose-equation-e403584e-2ba1-4194-8590-22be7db91d74");katex.render("x_{i,j}", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ is the point from the sampler at column $var element = document.getElementById("moose-equation-220aca3a-8d28-4010-94d0-a7a9bb3f659a");katex.render("i", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ and sample $var element = document.getElementById("moose-equation-b0fb6cf7-017f-4d18-82bc-47d386d549e8");katex.render("j", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ , $var element = document.getElementById("moose-equation-ccaa82a1-ee44-4f36-9959-f50bd08eabbf");katex.render("y_j", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ is the result of the full model at sample $var element = document.getElementById("moose-equation-77b51ebb-525b-436e-ad1d-17a02fd302a2");katex.render("j", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ , $var element = document.getElementById("moose-equation-a110402a-ad90-44dd-81e1-87715a07a323");katex.render("N", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ is the number of training samples, and $var element = document.getElementById("moose-equation-cb1aa362-1635-48d4-8249-647bde9b9cb9");katex.render("D", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ is the number of sampler columns. NearestPointSurrogate then uses this array to find the closest point to the given input point and outputs the full model result.

Example Input File Syntax

A sampler is created to produce points for the training data:

[Samplers<<<{"href": "../../syntax/Samplers/index.html"}>>>]
  [sample]
    type = CartesianProduct<<<{"description": "Provides complete Cartesian product for the supplied variables.", "href": "../samplers/CartesianProductSampler.html"}>>>
    linear_space_items<<<{"description": "A list of triplets, each item should include the min, step size, and number of steps."}>>> = '0 1 10
                          0 1 10
                          0 1 10'
  []
[]

The example full model is the GFunction vector postprocessor, see SobolStatistics for more details. This vector postprocessor takes in the sampler and evaluates the function at the training points.

[VectorPostprocessors<<<{"href": "../../syntax/VectorPostprocessors/index.html"}>>>]
  [values]
    type = GFunction
    sampler = sample
    q_vector = '0 0 0'
    execute_on = INITIAL
    outputs = none
  []
[]

The trainer then takes in the data from the sampler and the results from the vector postprocessor to create the _sample_points array:

[Trainers<<<{"href": "../../syntax/Trainers/index.html"}>>>]
  [train]
    type = NearestPointTrainer<<<{"description": "Loops over and saves sample values for [NearestPointSurrogate.md].", "href": "NearestPointTrainer.html"}>>>
    sampler<<<{"description": "Sampler used to create predictor and response data."}>>> = sample
    response<<<{"description": "Reporter value of response results, can be vpp with <vpp_name>/<vector_name> or sampler column with 'sampler/col_<index>'."}>>> = values/g_values
  []
[]

We then output the training data to file:

[Outputs<<<{"href": "../../syntax/Outputs/index.html"}>>>]
  [out]
    type = SurrogateTrainerOutput<<<{"description": "Output for trained surrogate model data.", "href": "../outputs/SurrogateTrainerOutput.html"}>>>
    trainers<<<{"description": "A list of SurrogateTrainer objects to output."}>>> = 'train'
    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."}>>> = FINAL
  []
[]

Input Parameters

responseReporter value of response results, can be vpp with / or sampler column with 'sampler/col_'.
C++ Type:ReporterName
Controllable:No
Description:Reporter value of response results, can be vpp with / or sampler column with 'sampler/col_'.
samplerSampler used to create predictor and response data.
C++ Type:SamplerName
Controllable:No
Description:Sampler used to create predictor and response data.

Required Parameters

converged_reporterReporter value used to determine if a sample's multiapp solve converged.
C++ Type:ReporterName
Controllable:No
Description:Reporter value used to determine if a sample's multiapp solve converged.
cv_n_trials1Number of repeated trials of cross-validation to perform.
Default:1
C++ Type:unsigned int
Controllable:No
Description:Number of repeated trials of cross-validation to perform.
cv_seed4294967295Seed used to initialize random number generator for data splitting during cross validation.
Default:4294967295
C++ Type:unsigned int
Controllable:No
Description:Seed used to initialize random number generator for data splitting during cross validation.
cv_splits10Number of splits (k) to use in k-fold cross-validation.
Default:10
C++ Type:unsigned int
Controllable:No
Description:Number of splits (k) to use in k-fold cross-validation.
cv_surrogateName of Surrogate object used for model cross-validation.
C++ Type:UserObjectName
Controllable:No
Description:Name of Surrogate object used for model cross-validation.
cv_typenoneCross-validation method to use for dataset. Options are 'none' or 'k_fold'.
Default:none
C++ Type:MooseEnum
Options:none, k_fold
Controllable:No
Description:Cross-validation method to use for dataset. Options are 'none' or 'k_fold'.
filenameThe name of the file which will be associated with the saved/loaded data.
C++ Type:FileName
Controllable:No
Description:The name of the file which will be associated with the saved/loaded data.
predictor_colsSampler columns used as the independent random variables, If 'predictors' and 'predictor_cols' are both empty, all sampler columns are used.
C++ Type:std::vector<unsigned int>
Controllable:No
Description:Sampler columns used as the independent random variables, If 'predictors' and 'predictor_cols' are both empty, all sampler columns are used.
predictorsReporter values used as the independent random variables, If 'predictors' and 'predictor_cols' are both empty, all sampler columns are used.
C++ Type:std::vector<ReporterName>
Controllable:No
Description:Reporter values used as the independent random variables, If 'predictors' and 'predictor_cols' are both empty, all sampler columns are used.
response_typerealResponse data type.
Default:real
C++ Type:MooseEnum
Options:real, vector_real
Controllable:No
Description:Response data type.
skip_unconverged_samplesFalseTrue to skip samples where the multiapp did not converge, 'stochastic_reporter' is required to do this.
Default:False
C++ Type:bool
Controllable:No
Description:True to skip samples where the multiapp did not converge, 'stochastic_reporter' is required to do this.

Optional Parameters

allow_duplicate_execution_on_initialFalseIn the case where this UserObject is depended upon by an initial condition, allow it to be executed twice during the initial setup (once before the IC and again after mesh adaptivity (if applicable).
Default:False
C++ Type:bool
Controllable:No
Description:In the case where this UserObject is depended upon by an initial condition, allow it to be executed twice during the initial setup (once before the IC and again after mesh adaptivity (if applicable).
execute_onTIMESTEP_ENDThe 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.
Default:TIMESTEP_END
C++ Type:ExecFlagEnum
Options:XFEM_MARK, FORWARD, ADJOINT, HOMOGENEOUS_FORWARD, ADJOINT_TIMESTEP_BEGIN, ADJOINT_TIMESTEP_END, NONE, INITIAL, LINEAR, LINEAR_CONVERGENCE, NONLINEAR, NONLINEAR_CONVERGENCE, POSTCHECK, TIMESTEP_END, TIMESTEP_BEGIN, MULTIAPP_FIXED_POINT_END, MULTIAPP_FIXED_POINT_BEGIN, MULTIAPP_FIXED_POINT_CONVERGENCE, FINAL, CUSTOM
Controllable:No
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.
execution_order_group0Execution order groups are executed in increasing order (e.g., the lowest number is executed first). Note that negative group numbers may be used to execute groups before the default (0) group. Please refer to the user object documentation for ordering of user object execution within a group.
Default:0
C++ Type:int
Controllable:No
Description:Execution order groups are executed in increasing order (e.g., the lowest number is executed first). Note that negative group numbers may be used to execute groups before the default (0) group. Please refer to the user object documentation for ordering of user object execution within a group.
force_postauxFalseForces the UserObject to be executed in POSTAUX
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in POSTAUX
force_preauxFalseForces the UserObject to be executed in PREAUX
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in PREAUX
force_preicFalseForces the UserObject to be executed in PREIC during initial setup
Default:False
C++ Type:bool
Controllable:No
Description:Forces the UserObject to be executed in PREIC during initial setup

Execution Scheduling 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.
use_displaced_meshFalseWhether or not this object should use the displaced mesh for computation. Note that in the case this is true but no displacements are provided in the Mesh block the undisplaced mesh will still be used.
Default:False
C++ Type:bool
Controllable:No
Description:Whether or not this object should use the displaced mesh for computation. Note that in the case this is true but no displacements are provided in the Mesh block the undisplaced mesh will still be used.

Advanced Parameters

prop_getter_suffixAn optional suffix parameter that can be appended to any attempt to retrieve/get material properties. The suffix will be prepended with a '_' character.
C++ Type:MaterialPropertyName
Unit:(no unit assumed)
Controllable:No
Description:An optional suffix parameter that can be appended to any attempt to retrieve/get material properties. The suffix will be prepended with a '_' character.
use_interpolated_stateFalseFor the old and older state use projected material properties interpolated at the quadrature points. To set up projection use the ProjectedStatefulMaterialStorageAction.
Default:False
C++ Type:bool
Controllable:No
Description:For the old and older state use projected material properties interpolated at the quadrature points. To set up projection use the ProjectedStatefulMaterialStorageAction.

Material Property Retrieval Parameters

Input Files

(modules/stochastic_tools/examples/surrogates/cross_validation/all_trainers_uniform_cv.i)
(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/train.i)
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/predictor_response.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/np_vec.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/cartesian.i)

(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/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/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/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/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/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/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/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/surrogates/nearest_point/predictor_response.i)

[StochasticTools]
[]

[Samplers]
  [sample]
    type = CartesianProduct
    linear_space_items = '0 1 10
                          0 1 10
                          0 1 10'
  []
  [test]
    type = CartesianProduct
    linear_space_items = '0.25 1 10
                          0.25 1 10
                          0.25 1 10'
  []
[]

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

[Reporters]
  [results]
    type = EvaluateSurrogate
    model = surrogate
    sampler = test
    parallel_type = ROOT
    execute_on = final
  []
[]

[Trainers]
  [train]
    type = NearestPointTrainer
    sampler = sample
    predictors = 'sampler_data/sample_0'
    predictor_cols = '1 2'
    response = values/g_values
  []
[]

[Surrogates]
  [surrogate]
    type = NearestPointSurrogate
    trainer = train
  []
[]

[Outputs]
  csv = true
  execute_on = final
[]

(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/surrogates/nearest_point/cartesian.i)

[StochasticTools]
[]

[Samplers]
  [sample]
    type = CartesianProduct
    linear_space_items = '0 1 10'
  []
  [test]
    type = CartesianProduct
    linear_space_items = '0.25 0.5 20'
  []
[]

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

[Reporters]
  [results]
    type = EvaluateSurrogate
    model = surrogate
    sampler = test
    parallel_type = ROOT
    execute_on = final
  []
[]

[Surrogates]
  [surrogate]
    type = NearestPointSurrogate
    trainer = train
  []
[]

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

[Outputs]
  csv = true
  execute_on = FINAL
[]

Overview
Example Input File Syntax
Input Parameters
Input Files