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Dakota Reference Manual
Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Gaussian Process Adaptive Importance Sampling
This keyword is related to the topics:
Alias: gaussian_process_adaptive_importance_sampling
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | build_samples | Number of initial model evaluations used in build phase | ||
| Optional | seed | Seed of the random number generator | ||
| Optional | samples_on_emulator | Number of samples at which to evaluate an emulator (surrogate) | ||
| Optional | import_build_points_file | File containing points you wish to use to build a surrogate | ||
| Optional | export_approx_points_file | Output file for evaluations of a surrogate model | ||
| Optional | response_levels | Values at which to estimate desired statistics for each response | ||
| Optional | max_iterations | Stopping criterion based on number of iterations | ||
| Optional | distribution | Selection of cumulative or complementary cumulative functions | ||
| Optional | probability_levels | Specify probability levels at which to estimate the corresponding response value | ||
| Optional | gen_reliability_levels | Specify generalized relability levels at which to estimate the corresponding response value | ||
| Optional | rng | Selection of a random number generator | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
gpais is recommended for problems that have a relatively small number of input variables (e.g. less than 10-20). This method, Gaussian Process Adaptive Importance Sampling, is outlined in the paperDalbey2012.
This method starts with an initial set of LHS samples and adds samples one at a time, with the goal of adaptively improving the estimate of the ideal importance density during the process. The approach uses a mixture of component densities. An iterative process is used to construct the sequence of improving component densities. At each iteration, a Gaussian process (GP) surrogate is used to help identify areas in the space where failure is likely to occur. The GPs are not used to directly calculate the failure probability; they are only used to approximate the importance density. Thus, the Gaussian process adaptive importance sampling algorithm overcomes limitations involving using a potentially inaccurate surrogate model directly in importance sampling calculations.
These keywords may also be of interest:
1.8.4 
