Engineering with Computers

, Volume 26, Issue 1, pp 81–98 | Cite as

Multiobjective global surrogate modeling, dealing with the 5-percent problem

  • Dirk Gorissen
  • Ivo Couckuyt
  • Eric Laermans
  • Tom Dhaene
Original Article

Abstract

When dealing with computationally expensive simulation codes or process measurement data, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization, prototyping and optimization. Typically the model parameter (=hyperparameter) optimization problem as part of global surrogate modeling is formulated in a single objective way. Models are generated according to a single objective (accuracy). However, this requires an engineer to determine a single accuracy target and measure upfront, which is hard to do if the behavior of the response is unknown. Likewise, the different outputs of a multi-output system are typically modeled separately by independent models. Again, a multiobjective approach would benefit the domain expert by giving information about output correlation and enabling automatic model type selection for each output dynamically. With this paper the authors attempt to increase awareness of the subtleties involved and discuss a number of solutions and applications. In particular, we present a multiobjective framework for global surrogate model generation to help tackle both problems and that is applicable in both the static and sequential design (adaptive sampling) case.

Keywords

Surrogate modeling Metamodeling Error estimation Performance measures Model selection Multiobjective optimization Adaptive sampling Hyperparameter optimization 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Dirk Gorissen
    • 1
  • Ivo Couckuyt
    • 1
  • Eric Laermans
    • 1
  • Tom Dhaene
    • 1
  1. 1.Department of Information Technology (INTEC)IBBT, Ghent UniversityGhentBelgium

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