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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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(Experimental) How to select new points
Alias: none
Argument(s): none
Default: naive
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Required (Choose One) | Batch Selection Criterion (Group 1) | naive | Take the highest scoring candidates | |
distance_penalty | Add a penalty to spread out the points in the batch | |||
topology | In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select. | |||
constant_liar | Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points. |
adaptive_sampling
is an experimental capability that is not ready for production use at this time.
With batch or multi-point selection, the true model can be evaluated in parallel and thus increase throughput before refitting our surrogate model. This proposes a new challenge as the problem of choosing a single point and choosing multiple points off a surrogate are fundamentally different. Selecting the n
best scoring candidates is more than likely to generate a set of points clustered in one area which will not be conducive to adapting the surrogate.
We have implemented several strategies for batch selection of points. These are described in the User's manual and are the subject of active research.
The batch_selection
strategies include:
naive:
distance_penalty
constant_liar
topology