Dakota Reference Manual: experimental_gaussian_process

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
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.