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 number of samples for sampling-based methods |
||
Required (Choose One) |
Approximation |
Create GP surrogate in x-space |
|
Create GP surrogate in u-space |
|||
Optional (Choose One) |
GP Implementation |
Use the Surfpack version of Gaussian Process surrogates |
|
Select the built in Gaussian Process surrogate |
|||
Use the experimental Gaussian Process surrogate |
|||
Optional |
File containing points you wish to use to build a surrogate |
||
Optional |
Output file for surrogate model value evaluations |
||
Optional |
Use derivative data to construct surrogate models |
||
Optional |
Seed of the random number generator |
||
Optional |
Selection of a random number generator |
||
Optional |
Values at which to estimate desired statistics for each response |
||
Optional |
Specify probability levels at which to estimate the corresponding response value |
||
Optional |
Specify generalized relability levels at which to estimate the corresponding response value |
||
Optional |
Selection of cumulative or complementary cumulative functions |
||
Optional |
Number of iterations allowed for optimizers and adaptive UQ methods |
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Optional |
Stopping criterion based on objective function or statistics convergence |
||
Optional |
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.