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 samplingbased methods 

Required (Choose One) 
Approximation 
Create GP surrogate in xspace 

Create GP surrogate in uspace 

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 

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 uspace. Rather
they use a Gaussian process model to form an approximation to the
limit state (based either in xspace via the x_gaussian_process
specification or in uspace 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.