global_interval_est
Interval analysis using global optimization methods
Topics
uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation
Specification
Alias: nond_global_interval_est
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional |
Number of samples for sampling-based methods |
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Optional |
Seed of the random number generator |
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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 |
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Optional |
Number of function evaluations allowed for optimizers |
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Optional (Choose One) |
Solution Approach |
Use the surrogate based optimization method |
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Use the Efficient Global Optimization method |
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Use an evolutionary algorithm |
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Uses Latin Hypercube Sampling (LHS) to sample variables |
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Optional |
Selection of a random number generator |
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Optional |
Identifier for model block to be used by a method |
Description
In the global approach to interval estimation, one uses either a global optimization method or a sampling method to assess the bounds of the responses.
global_interval_est
allows the user to specify several approaches to calculate
interval bounds on the output responses.
lhs
- note: this takes the minimum and maximum of the samples as the boundsego
sbo
ea
Additional Resources
Refer to Variable Support for information on supported variable types.