GaussianProcessData

The GaussianProcessData object simply coverts a the covariance function hyperparameters into a VectorPostprocessor for post regression analysis of hyperparameters.

Example Syntax

[VectorPostprocessors<<<{"href": "../../syntax/VectorPostprocessors/index.html"}>>>]
  [hyperparams]
    type = GaussianProcessData
    gp_name = 'GP_avg'
    execute_on = final
  []
  [data]
    type = SamplerData<<<{"description": "Tool for extracting Sampler object data and storing in VectorPostprocessor vectors.", "href": "SamplerData.html"}>>>
    sampler<<<{"description": "The sample from which to extract distribution data."}>>> = test_sample
    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."}>>> = 'initial timestep_end'
  []
[]

warning

The detailed documentation of this object is only available when Moose is compiled with Libtorch. For instructions on how to compile Moose with Libtorch, visit the general installation webpage or click here.

(moose/modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_tuned_adam.i)

[StochasticTools]
[]

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

[Samplers]
  [train_sample]
    type = MonteCarlo
    num_rows = 20
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
  [test_sample]
    type = MonteCarlo
    num_rows = 100
    distributions = 'k_dist q_dist'
    execute_on = PRE_MULTIAPP_SETUP
    seed = 100
  []
[]

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

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

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

[Reporters]
  [results]
    type = StochasticReporter
    parallel_type = ROOT
  []
  [samp_avg]
    type = EvaluateSurrogate
    model = GP_avg
    sampler = test_sample
    evaluate_std = 'true'
    parallel_type = ROOT
    execute_on = final
  []
  [train_avg]
    type = EvaluateSurrogate
    model = GP_avg
    sampler = train_sample
    evaluate_std = 'true'
    parallel_type = ROOT
    execute_on = final
  []
[]

[VectorPostprocessors]
  [hyperparams]
    type = GaussianProcessData
    gp_name = 'GP_avg'
    execute_on = final
  []
  [data]
    type = SamplerData
    sampler = test_sample
    execute_on = 'initial timestep_end'
  []
[]

[Trainers]
  [GP_avg_trainer]
    type = GaussianProcessTrainer
    execute_on = timestep_end
    covariance_function = 'covar' #Choose a squared exponential for the kernel
    standardize_params = 'true' #Center and scale the training params
    standardize_data = 'true' #Center and scale the training data
    sampler = train_sample
    response = results/data:avg:value
    tune_parameters = 'covar:signal_variance covar:length_factor'
    num_iters = 1000
    batch_size = 20
    learning_rate = 0.005
  []
[]

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

[Covariance]
  [covar]
    type = SquaredExponentialCovariance
    signal_variance = 1.0 #Use a signal variance of 1 in the kernel
    noise_variance = 1e-6 #A small amount of noise can help with numerical stability
    length_factor = '1.0 1.0' #Select a length factor for each parameter (k and q)
  []
[]

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

Example Syntax