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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Hierarchical approximations use corrected results from a low fidelity model as an approximation to the results of a high fidelity "truth" model.
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Required | low_fidelity_model_pointer | Pointer to low fidelity model | ||
| Required | high_fidelity_model_pointer | Pointer to high fidelity model | ||
| Required | correction | Correction approaches for surrogate models | ||
Hierarchical approximations use corrected results from a low fidelity model as an approximation to the results of a high fidelity "truth" model. These approximations are also known as model hierarchy, multifidelity, variable fidelity, and variable complexity approximations. The required low_fidelity_model_pointer specification points to the low fidelity model specification. This model is used to generate low fidelity responses which are then corrected and returned to an iterator. The required high_fidelity_model_pointer specification points to the specification for the high fidelity truth model. This model is used only for verifying low fidelity results and updating low fidelity corrections. The correction specification specifies which correction technique will be applied to the low fidelity results in order to match the high fidelity results at one or more points. In the hierarchical case (as compared to the global case), the correction specification is required, since the omission of a correction technique would effectively eliminate the purpose of the high fidelity model. If it is desired to use a low fidelity model without corrections, then a hierarchical approximation is not needed and a single model should be used. Refer to global for additional information on available correction approaches.
Multifidelity Surrogates : Multifidelity modeling involves the use of a low-fidelity physics-based model as a surrogate for the original high-fidelity model. The low-fidelity model typically involves a coarser mesh, looser convergence tolerances, reduced element order, or omitted physics. It is a separate model in its own right and does not require data from the high-fidelity model for construction. Rather, the primary need for high-fidelity evaluations is for defining correction functions that are applied to the low-fidelity results.
Multifidelity Surrogate Models
A second type of surrogate is the {model hierarchy} type (also called multifidelity, variable fidelity, variable complexity, etc.). In this case, a model that is still physics-based but is of lower fidelity (e.g., coarser discretization, reduced element order, looser convergence tolerances, omitted physics) is used as the surrogate in place of the high-fidelity model. For example, an inviscid, incompressible Euler CFD model on a coarse discretization could be used as a low-fidelity surrogate for a high-fidelity Navier-Stokes model on a fine discretization.
These keywords may also be of interest:
1.8.4 
