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  4. Aspects of adaptive hierarchical RBF metamodels for optimization
 
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2012
Zeitschriftenaufsatz
Titel

Aspects of adaptive hierarchical RBF metamodels for optimization

Abstract
Radial basis functions (RBFs), among other techniques, are used to construct metamodels that approximate multi-objective expensive high-fidelity functions from a finite number of function evaluations (design of experiments, DoE). Radial basis functions can be applied if the DoE covers the parameter space in an arbitrary though uniform manner. Leave-one-out strategies allow for computing tolerance limits. The approximated value and a certain tolerance can be interpreted as expectation and variance of a random experiment. Thus, model improvement as described for Kriging models in the literature can in principal be applied to RBF-based metamodels, too. We describe our adaptive and hierarchical metamodelling approach that deals with the specific problems that such metamodel adaptions pose to R BF-based models. We also briefly discuss implementation details and first industrial test cases.
Author(s)
Bühren, Georg van
Hornung, Nils
Clees, Tanja
Nikitina, Lialia
Zeitschrift
Journal of computational methods in sciences and engineering : JCMSE
Thumbnail Image
DOI
10.3233/JCM-2012-0401
Language
Englisch
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SCAI
Tags
  • surrogate-based optim...

  • RBF interpolation

  • metamodel refinement

  • hierarchical metamode...

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