AddCovarianceAction

This action adds CovarianceFunctionBase asds objects contained within the [Covariance] block. For example, the following block adds a SquaredExponentialCovariance covariance function.

[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)
  []
[]

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_training.i)

[StochasticTools]
[]

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

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

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

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

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

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

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

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

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