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Dakota Reference Manual
Version 6.16
Explore and Predict with Confidence
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Use the Gaussian process regression surrogate from the surrogates module
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 | ||
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.