Ensemble of surrogates

  • Goel T
  • Haftka R
  • Shyy W
 et al. 
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Abstract

The custom in surrogate-based modeling of complex engineering problems is to fit one or more surrogate models and select the one surrogate model that performs best. In this paper, we extend the utility of an ensemble of surrogates to (1) identify regions of possible high errors at locations where predictions of surrogates widely differ, and (2) provide a more robust approximation approach. We explore the possibility of using the best surrogate or a weighted average surrogate model instead of individual surrogate models. The weights associated with each surrogate model are determined based on the errors in surrogates. We demonstrate the advantages of an ensemble of surrogates using analytical problems and one engineering problem. We show that for a single problem the choice of test surrogate can depend on the design of experiments.

Author-supplied keywords

  • Kriging
  • Multiple surrogate models
  • Polynomial response surfaces
  • Radial basis neural networks

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Authors

  • Tushar Goel

  • Raphael T. Haftka

  • Wei Shyy

  • Nestor V. Queipo

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