Approximation methods in multidisciplinary analysis and optimization: a panel discussion

被引:0
作者
T.W. Simpson
A.J. Booker
D. Ghosh
A.A. Giunta
P.N. Koch
R.-J. Yang
机构
[1] The Pennsylvania State University,Department of Mechanical & Nuclear Engineering
[2] University Park,Optimization and Uncertainty Estimation Department
[3] The Boeing Company,Advanced Technologies and Applications
[4] Vanderplaats Research & Development,Optimization & Robustness, Safety R & A
[5] Inc.,undefined
[6] Sandia National Laboratories,undefined
[7] Engineous Software,undefined
[8] Inc.,undefined
[9] Ford Research & Advanced Engineering,undefined
来源
Structural and Multidisciplinary Optimization | 2004年 / 27卷
关键词
analysis of variance; approximation methods; design of experiments; kriging; response surfaces; surrogate models ;
D O I
暂无
中图分类号
学科分类号
摘要
This paper summarizes the discussion at the Approximation Methods Panel that was held at the 9thAIAA/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:11
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