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-7a39781b-f45a-47df-8133-c1ed1e87f3c1");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:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});

where $var element = document.getElementById("moose-equation-b15d4893-bf4d-4b56-99c6-345a32b80634");katex.render("x_{i,j}", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ is the point from the sampler at column $var element = document.getElementById("moose-equation-69ef4aeb-a8d5-4ab4-8721-4274f1f1ed34");katex.render("i", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ and sample $var element = document.getElementById("moose-equation-9cefb49d-b7db-4312-8c3a-5b016f033dd8");katex.render("j", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ , $var element = document.getElementById("moose-equation-f9dd4a58-a3a0-4509-9044-299da1b15679");katex.render("y_j", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ is the result of the full model at sample $var element = document.getElementById("moose-equation-24c9fb00-f8bf-47e1-b076-4dc33db8eb3d");katex.render("j", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ , $var element = document.getElementById("moose-equation-093edd2b-2c12-46b6-b3b2-87a26f1c42ab");katex.render("N", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ is the number of training samples, and $var element = document.getElementById("moose-equation-27de4283-55d4-41e5-a98d-3d77849fb919");katex.render("D", element, {displayMode:false,throwOnError:false,macros:{"\\pf":"\\frac{\\partial #1}{\\partial #2}"}});$ 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]
  [sample]
    type = CartesianProduct
    linear_space_items = '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]
  [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]
  [train]
    type = NearestPointTrainer
    sampler = sample
    response = values/g_values
  []
[]

We then output the training data to file:

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

Input Parameters

responseReporter value of response results, can be vpp with / or sampler column with 'sampler/col_'.
C++ Type:ReporterName
Options:
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
Options:
Description:Sampler used to create predictor and response data.

Required Parameters

execute_onTIMESTEP_ENDThe list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM.
Default:TIMESTEP_END
C++ Type:ExecFlagEnum
Options:NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM
Description:The list of flag(s) indicating when this object should be executed, the available options include NONE, INITIAL, LINEAR, NONLINEAR, TIMESTEP_END, TIMESTEP_BEGIN, FINAL, CUSTOM.
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>
Options:
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>
Options:
Description:Reporter values used as the independent random variables, If 'predictors' and 'predictor_cols' are both empty, all sampler columns are used.

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
Options:
Description:In the case where this UserObject is depended upon by an initial condition, allow it to be executed twice during the initial setup (once before the IC and again after mesh adaptivity (if applicable).
control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Options:
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Options:
Description:Set the enabled status of the MooseObject.
force_preauxFalseForces the GeneralUserObject to be executed in PREAUX
Default:False
C++ Type:bool
Options:
Description:Forces the GeneralUserObject to be executed in PREAUX
force_preicFalseForces the GeneralUserObject to be executed in PREIC during initial setup
Default:False
C++ Type:bool
Options:
Description:Forces the GeneralUserObject to be executed in PREIC during initial setup
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
Options:
Description:Whether or not this object should use the displaced mesh for computation. Note that in the case this is true but no displacements are provided in the Mesh block the undisplaced mesh will still be used.

Advanced Parameters

Input Files

(modules/stochastic_tools/test/tests/surrogates/nearest_point/predictor_response.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/cartesian.i)
(modules/stochastic_tools/examples/surrogates/nearest_point_training.i)
(modules/stochastic_tools/examples/surrogates/combined/trans_diff_2d/trans_diff_trainer.i)
(modules/stochastic_tools/test/tests/surrogates/nearest_point/train.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/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
  []
  [results]
    type = EvaluateSurrogate
    model = surrogate
    sampler = test
    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/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
  []
  [results]
    type = EvaluateSurrogate
    model = surrogate
    sampler = test
    output_samples = true
    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
[]

(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
    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/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
    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/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
  []
[]

Overview
Example Input File Syntax
Input Parameters
Input Files

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NearestPointTrainer

Overview

Example Input File Syntax

Input Parameters

Required Parameters

Optional Parameters

Advanced Parameters

Input Files