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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Construct a surrogate from multiple existing training points
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Multipoint Surrogate (Group 1) | tana | Local multi-point model via two-point nonlinear approximation | |
qmea | Multi-point surrogate approximation based on QMEA algorithm | |||
Required | actual_model_pointer | Pointer to specify a "truth" model, from which to construct a surrogate |
Multipoint approximations use data from previous design points to improve the accuracy of local approximations. The data often comes from the current and previous iterates of a minimization algorithm.
Currently, only the Two-point Adaptive Nonlinearity Approximation (TANA-3) method of [95] is supported with the tana
keyword.
The truth model to be used to generate the value/gradient data used in the approximation is identified through the required actual_model_pointer
specification.
These keywords may also be of interest: