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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Build a locally accurate surrogate from data at a single point
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Required | taylor_series | Construct a Taylor Series expansion around a point | ||
| Required | actual_model_pointer | Pointer to specify a "truth" model, from which to construct a surrogate | ||
Local approximations use value, gradient, and possibly Hessian data from a single point to form a series expansion for approximating data in the vicinity of this point.
The currently available local approximation is the taylor_series selection.
The truth model to be used to generate the value/gradient/Hessian data used in the series expansion is identified through the required actual_model_pointer specification. The use of a model pointer (as opposed to an interface pointer) allows additional flexibility in defining the approximation. In particular, the derivative specification for the truth model may differ from the derivative specification for the approximation, and the truth model results being approximated may involve a model recursion (e.g., the values/gradients from a nested model).
These keywords may also be of interest:
1.8.4 
