Metamodels for computer-based engineering design: Survey and recommendations

T. W. Simpson, J. D. Peplinski, P. N. Koch, J. K. Allen

Research output: Contribution to journalReview article

1383 Scopus citations

Abstract

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of today's engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper, we review several of these techniques, including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning and kriging. We survey their existing application in engineering design, and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations, and how common pitfalls can be avoided.

Original languageEnglish (US)
Pages (from-to)129-150
Number of pages22
JournalEngineering with Computers
Volume17
Issue number2
DOIs
StatePublished - Jan 1 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Engineering(all)
  • Computer Science Applications

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