Dakota Reference Manual: global_reliability

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global_reliability

Global reliability methods

## Topics

This keyword is related to the topics:

- uncertainty_quantification
- reliability_methods

## Specification

Alias: nond_global_reliability

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | initial_samples | Initial number of samples for sampling-based methods
| Required (Choose One) | Approximation (Group 1) | x_gaussian_process | Create GP surrogate in x-space
u_gaussian_process | Create GP surrogate in u-space
| Optional (Choose One) | GP Implementation (Group 2) | surfpack | Use the Surfpack version of Gaussian Process surrogates
dakota | Select the built in Gaussian Process surrogate
experimental | Use the experimental Gaussian Process 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 | use_derivatives | Use derivative data to construct surrogate models
| Optional | seed | Seed of the random number generator
| Optional | rng | Selection of a random number generator
| 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 | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods
| Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence
| Optional | model_pointer | Identifier for model block to be used by a method

## Description

These methods do not support forward/inverse mappings involving `reliability_levels`, since they never form a reliability index based on distance in u-space. Rather they use a Gaussian process model to form an approximation to the limit state (based either in x-space via the `x_gaussian_process` specification or in u-space via the `u_gaussian_process` specification), followed by probability estimation based on multimodal adaptive importance sampling (see [9]) and [10]). These probability estimates may then be transformed into generalized reliability levels if desired. At this time, inverse reliability analysis (mapping probability or generalized reliability levels into response levels) is not implemented.

The Gaussian process model approximation to the limit state is formed over the aleatory uncertain variables by default, but may be extended to also capture the effect of design, epistemic uncertain, and state variables. If this is desired, one must use the appropriate controls to specify the active variables in the variables specification block. Refer to variable_support for additional information on supported variable types.

## See Also

These keywords may also be of interest:

- adaptive_sampling
- gpais
- local_reliability
- sampling
- importance_sampling
- polynomial_chaos
- stoch_collocation