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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Reuses the same seed value for multiple random sampling sets
Alias: none
Argument(s): none
Default: not fixed; pattern varies run-to-run
The fixed_seed
flag is relevant if multiple sampling sets will be generated over the coarse of a Dakota analysis. This occurs when using advance methods (e.g., surrogate-based optimization, optimization under uncertainty). The same seed value is reused for each of these multiple sampling sets, which can be important for reducing variability in the sampling results.
Default Behavior
The default behavior is to not use a fixed seed, as the repetition of the same sampling pattern can result in a modeling weakness that an optimizer could potentially exploit (resulting in actual reliabilities that are lower than the estimated reliabilities). For repeatable studies, the seed
must also be specified.
method sampling sample_type lhs samples = 10 fixed_seed