Towards an intelligent non-stationary performance prediction of engineering systems

  • Authors:

  • David J. J. Toal;Andy J. Keane

  • Affiliations:

  • Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Southampton, U.K.;Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Southampton, U.K.

  • Venue:
  • LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
  • Year:
  • 2011

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Abstract

The analysis of complex engineering systems can often be expensive thereby necessitating the use of surrogate models within any design optimization. However, the time variant response of quantities of interest can be non-stationary in nature and therefore difficult to represent effectively with traditional surrogate modelling techniques. The following paper presents the application of partial non-stationary kriging to the prediction of time variant responses where the definition of the non-linear mapping scheme is based upon prior knowledge of either the inputs to, or the nature of, the engineering system considered.