Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Surrogate model training data reuse control


This keyword is related to the topics:


Alias: reuse_samples

Argument(s): none

Default: all for import; none otherwise

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
(Choose One)
Group 1 all Option for reuse_points
region Option for reuse_points
none Option for reuse_points


Dakota's global surrogate methods rely on training data, which can either come from evaluation of a "truth" model, which is generated by the method specified with dace_method_pointer, from a file of existing training data, identified by import_points_file, or both.

The reuse_points keyword controls the amount of training data used in building a surrogate model, either initially, or during iterative rebuild, as in surrogate-based optimization. If import_points_file is specified, reuse_points controls how the file contents are used. If used during iterative rebuild, it controls what data from previous surrogate builds is reused in building the current model.

  • all (default for file import) - use all points in the file or available from previous builds
  • region - use only the points falling in the current trust region (see surrogate_based_local)
  • none (default when no import) - ignore the contents of the file or previous build points, and gather new training data using the specified DACE method