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 |
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Optional |
This keyword enables the use of deterministic polynomial trend function |
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Optional |
Number of optimization restarts for L-BFGS-B |
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Optional (Choose One) |
Nugget |
Value for the fixed nugget parameter |
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Use regression to estimate the nugget. |
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Optional |
Filename for a YAML file that specifies Gaussian process options |
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Optional |
Output file for surrogate model variance evaluations |
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Optional |
Exports surrogate model in user-specified format(s) |
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Optional |
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.