surrogate_based_uq
Generic UQ method for constructing and interrogating a surrogate model.
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional |
Number of samples at which to evaluate an emulator (surrogate) |
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Optional |
Selection of sampling strategy |
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Optional |
Selection of a random number generator |
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Optional |
Allow refinement of probability and generalized reliability results using importance sampling |
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Optional |
Output moments of the specified type and include them within the set of final statistics. |
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Optional |
Values at which to estimate desired statistics for each response |
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Optional |
Specify probability levels at which to estimate the corresponding response value |
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Optional |
Specify reliability levels at which the response values will be estimated |
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Optional |
Specify generalized relability levels at which to estimate the corresponding response value |
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Optional |
Selection of cumulative or complementary cumulative functions |
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Optional |
Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects |
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Optional (Choose One) |
Covariance Type |
Display only the diagonal terms of the covariance matrix |
|
Display the full covariance matrix |
|||
Optional |
Filename for points at which to evaluate the PCE/SC surrogate |
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Optional |
Output file for surrogate model value evaluations |
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Optional |
Seed of the random number generator |
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Optional |
Reuses the same seed value for multiple random sampling sets |
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Optional |
Identifier for model block to be used by a method |
Description
As surrogate models by stochastic expansion migrate into the model specification, this method provides a general-purpose UQ method to interrogate the surrogate for generating statistics.
This method must identify the surrogate of interest through its model_pointer
,
distinguishing it from fully-integrated method specifications such as
polynomial_chaos
, stoch_collocation
, and
function_train
that couple directly with a simulation model
(and form the PCE, SC, FT surrogate approximations implicitly prior to using
them for generating statistics).