article

Metamodels for Computer-based Engineering Design: Survey and recommendations

Authors:

J. D. Poplinski,

J. K. AllenAuthors Info & Claims

Engineering with Computers, Volume 17, Issue 2

Pages 129 - 150

https://doi.org/10.1007/PL00007198

Published: 01 July 2001 Publication History

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.

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Metamodels for Computer-based Engineering Design: Survey and recommendations
1. Mathematics of computing
  1. Probability and statistics

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cover image Engineering with Computers

Engineering with Computers Volume 17, Issue 2

July 2001

106 pages

ISSN:0177-0667

EISSN:1435-5663

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Copyright © Copyright © 2001 Springer-Verlag London Limited.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2001

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Keywords.Deterministic analysis; Engineering design; Kriging; Metamodels; Robust design; RSM

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