allocation_control
Sample allocation approach for multifidelity expansions
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
|---|---|---|---|
Required (Choose One) |
Multifidelity Sample Allocation Control |
Sample allocation based on greedy refinement within multifidelity function train |
Description
Multifidelity surrogate approaches, including polynomial chaos, stochastic collocation, and function train, can optionally employ a integrated greedy competition across the model sequence, where each model index can supply one or more refinement candidates which are competed to determine the candidate with the greatest impact on the QoI statistics per unit cost. This greedy competition implicitly determines the optimal sample allocation across model indices.
Default Behavior
The default, when allocation_control is not specified, is to
compute or adapt separately for each model index (individual instead
of integrated refinement).
