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<<<{"href": "../../syntax/Trainers/index.html"}>>>]
  [GP_avg_trainer]
    type = GaussianProcessTrainer<<<{"description": "Provides data preperation and training for a single- or multi-output Gaussian Process surrogate model.", "href": "../trainers/GaussianProcessTrainer.html"}>>>
    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."}>>> = timestep_end
    covariance_function<<<{"description": "Name of covariance function."}>>> = 'covar' #Choose a squared exponential for the kernel
    standardize_params<<<{"description": "Standardize (center and scale) training parameters (x values)"}>>> = 'true' #Center and scale the training params
    standardize_data<<<{"description": "Standardize (center and scale) training data (y values)"}>>> = 'true' #Center and scale the training data
    sampler<<<{"description": "Sampler used to create predictor and response data."}>>> = train_sample
    response<<<{"description": "Reporter value of response results, can be vpp with <vpp_name>/<vector_name> or sampler column with 'sampler/col_<index>'."}>>> = results/data:avg:value
  []
[]

[Covariance<<<{"href": "../../syntax/Covariance/index.html"}>>>]
  [covar]
    type = SquaredExponentialCovariance<<<{"description": "Squared Exponential covariance function.", "href": "../covariances/SquaredExponentialCovariance.html"}>>>
    signal_variance<<<{"description": "Signal Variance ($\\sigma_f^2$) to use for kernel calculation."}>>> = 1 #Use a signal variance of 1 in the kernel
    noise_variance<<<{"description": "Noise Variance ($\\sigma_n^2$) to use for kernel calculation."}>>> = 1e-6 #A small amount of noise can help with numerical stability
    length_factor<<<{"description": "Length factors to use for Covariance Kernel"}>>> = '0.38971 0.38971' #Select a length factor for each parameter (k and q)
  []
[]
(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<<<{"href": "../../syntax/Surrogates/index.html"}>>>]
  [GP_avg]
    type = GaussianProcessSurrogate<<<{"description": "Computes and evaluates Gaussian Process surrogate model.", "href": "../surrogates/GaussianProcessSurrogate.html"}>>>
    filename<<<{"description": "The name of the file which will be associated with the saved/loaded data."}>>> = 'gauss_process_training_GP_avg_trainer.rd'
  []
[]
(modules/stochastic_tools/test/tests/surrogates/gaussian_process/GP_squared_exponential_testing.i)