LoadCovarianceDataAction

This action operates on existing GaussianProcess objects contained within the [Surrogates] block. If the model provides a filename (as shown below), a [Covariance] object equivalent to the function used in the training phase is reconstructed for use in model evaluation.

Example Input File Syntax

In the training input file we setup a GaussianProcessTrainer, with a SquaredExponential covariance function.

[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)
  []
[]
(contrib/moose/modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_training.i)

In the surrogate input file, the GaussianProcess surrogate recreates the covariance function used in training and links to it.

[Surrogates]
  [GP_avg]
    type = GaussianProcessSurrogate
    filename = 'gauss_process_training_GP_avg_trainer.rd'
  []
[]
(contrib/moose/modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_testing.i)