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Approximation methods in multidisciplinary analysis and optimization: a panel discussion

delete2004-06-29
delete325
PRE
AI
T
Timothy W. Simpson
B
Booker, AJ
D
Dipankar Ghosh
G
Giunta, AA
K
Koch, PN
R
R. J. Yang
DOI:10.1007/s00158-004-0389-9delete
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Abstract

Abstract

En 中文
This paper summarizes the discussion at the Approximation Methods Panel that was held at the 9(th)AIAA/ISSMO Symposium on Multidisciplinary Analysis & Optimization in Atlanta, GA on September 2-4, 2002. The objective of the panel was to discuss the current state-of-the-art of approximation methods and identify future research directions important to the community. The panel consisted of five representatives from industry and government: (1) Andrew J. Booker from The Boeing Company, (2) Dipankar Ghosh from Vanderplaats Research & Development, (3) Anthony A. Giunta from Sandia National Laboratories, (4) Patrick N. Koch from Engineous Software, Inc., and (5) Ren-Jye Yang from Ford Motor Company. Each panelist was asked to (i) give one or two brief examples of typical uses of approximation methods by his company, (ii) describe the current state-of-the-art of these methods used by his company, (iii) describe the current challenges in the use and adoption of approximation methods within his company, and (iv) identify future research directions in approximation methods. Several common themes arose from the discussion, including differentiating between design of experiments and design and analysis of computer experiments, visualizing experimental results and data from approximation models, capturing uncertainty with approximation methods, and handling problems with large numbers of variables. These are discussed in turn along with the future directions identified by the panelists, which emphasized educating engineers in using approximation methods.
Keywords:
analysis of variance
approximation methods
design of experiments
kriging
response surfaces
surrogate models

Journal

Structural and Multidisciplinary Optimization cover
Structural and Multidisciplinary Optimization
IF:
4
Papers:
4.8K
Citations:
1.7W

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