Affiliations: Fraunhofer SCAI, Sankt Augustin, Germany | [x] Engineering Optimization and Modeling Center, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland | [y] Mathematics and Scientific Computing, National Physical Laboratory, Teddington, UK
Correspondence:
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Corresponding author: Nils Hornung, Fraunhofer SCAI, Sankt Augustin, Germany. E-mail: [email protected].
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 RBF-based models. We also briefly discuss implementation details and first industrial test cases.
Keywords: Slawomir Koziel, Leifur Leifsson, Xin-She Yang
