global_reliability

Global reliability methods

Topics

uncertainty_quantification, reliability_methods

Specification

  • Alias: nond_global_reliability

  • Arguments: 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

x_gaussian_process

Create GP surrogate in x-space

u_gaussian_process

Create GP surrogate in u-space

Optional (Choose One)

GP Implementation

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 [BES+07]) and [BES+08]). 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.