Dakota Reference Manual  Version 6.15
Explore and Predict with Confidence
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experimental_gaussian_process


Use the Gaussian process regression surrogate from the surrogates module

Specification

Alias: none

Argument(s): 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 (Group 1) 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.