experimental_gaussian_process

Use the Gaussian process regression surrogate from the surrogates module

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

trend

This keyword enables the use of deterministic polynomial trend function

Optional

num_restarts

Number of optimization restarts for L-BFGS-B

Optional (Choose One)

Nugget

nugget

Value for the fixed nugget parameter

find_nugget

Use regression to estimate the nugget.

Optional

options_file

Filename for a YAML file that specifies Gaussian process options

Optional

export_approx_variance_file

Output file for surrogate model variance evaluations

Optional

export_model

Exports surrogate model in user-specified format(s)

Optional

import_model

Import surrogate model from archive file

Description

This Gaussian process implementation is contained in Dakota’s surrogates module and is considered experimental. It uses gradient-based optimization with restarts to determine hyperparmeters and trend coefficients. Nugget and trend estimation are optional.