Dakota Reference Manual: gpais

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gpais

Gaussian Process Adaptive Importance Sampling

## Topics

This keyword is related to the topics:

- uncertainty_quantification

## Specification

Alias: gaussian_process_adaptive_importance_sampling

Argument(s): none

Child Keywords:

| 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 surrogate model value evaluations
| Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods
| Optional | response_levels | Values at which to estimate desired statistics for each response
| 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 | distribution | Selection of cumulative or complementary cumulative functions
| Optional | rng | Selection of a random number generator
| Optional | model_pointer | Identifier for model block to be used by a method

## Description

`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.

## See Also

These keywords may also be of interest:

- adaptive_sampling
- local_reliability
- global_reliability
- sampling
- importance_sampling
- polynomial_chaos
- stoch_collocation