Approximation methods in multidisciplinary analysis and optimization: a panel discussion

被引:282
作者
Simpson, TW
Booker, AJ
Ghosh, D
Giunta, AA
Koch, PN
Yang, RJ
机构
[1] Penn State Univ, Dept Mech & Nucl Engn, University Pk, PA 16802 USA
[2] Boeing Co, Seattle, WA 98124 USA
[3] Vanderplaats Res & Dev Inc, Colorado Springs, CO 80906 USA
[4] Sandia Natl Labs, Optimizat & Uncertainty Estimat Dept, Albuquerque, NM 87185 USA
[5] Engineous Software Inc, Adv Technol & Applicat, Cary, NC 27513 USA
[6] Ford Res & Adv Engn, Optimizat & Robustness Safety R&A, Dearborn, MI 48124 USA
关键词
analysis of variance; approximation methods; design of experiments; kriging; response surfaces; surrogate models;
D O I
10.1007/s00158-004-0389-9
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:302 / 313
页数:12
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