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Metamodels for Computer-Based Engineering Design: Survey and RecommendationsThe use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays 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.
Document ID
19990087092
Acquisition Source
Headquarters
Document Type
Preprint (Draft being sent to journal)
Authors
Simpson, Timothy W.
(Georgia Inst. of Tech. Atlanta, GA United States)
Peplinski, Jesse
(Georgia Inst. of Tech. Atlanta, GA United States)
Koch, Patrick N.
(Georgia Inst. of Tech. Atlanta, GA United States)
Allen, Janet K.
(Georgia Inst. of Tech. Atlanta, GA United States)
Date Acquired
September 6, 2013
Publication Date
December 19, 1997
Subject Category
Computer Programming And Software
Funding Number(s)
CONTRACT_GRANT: NSF DMI-96-12327
CONTRACT_GRANT: NGT-51102
CONTRACT_GRANT: NSF DMI-96-12365
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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