0
RESEARCH PAPERS

Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses

[+] Author and Article Information
Stella M. Clarke

Department of Industrial & Manufacturing Engineering,  The Pennsylvania State University, University Park, PA 16802

Jan H. Griebsch

Lehrstuhl für Effiziente Algorithmen, The Technical University of Munich, Munich, Germany

Timothy W. Simpson1

Departments of Mechanical & Nuclear and Industrial & Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802tws8@psu.edu

1

Corresponding author.

J. Mech. Des 127(6), 1077-1087 (Aug 13, 2004) (11 pages) doi:10.1115/1.1897403 History: Received October 07, 2003; Revised August 13, 2004

A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a “model of a model” to provide a fast surrogate for a computationally expensive computer code. Common metamodeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we investigate support vector regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is reviewed, and SVR approximations are compared against the aforementioned four metamodeling techniques using a test bed of 26 engineering analysis functions. SVR achieves more accurate and more robust function approximations than the four metamodeling techniques, and shows great potential for metamodeling applications, adding to the growing body of promising empirical performance of SVR.

FIGURES IN THIS ARTICLE
<>
Copyright © 2005 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Accounting for slack variables

Grahic Jump Location
Figure 2

Eighth-order one-dimensional function

Grahic Jump Location
Figure 3

Flowchart of the SVR algorithm

Grahic Jump Location
Figure 4

Fit of one-dimensional function using SVR

Grahic Jump Location
Figure 5

Comparison of errors between metamodeling techniques relative to SVR

Grahic Jump Location
Figure 6

Comparison of errors for linear functions relative to SVR

Grahic Jump Location
Figure 7

Comparison of errors for nonlinear functions relative to SVR

Grahic Jump Location
Figure 8

Comparison of normalized standard deviations of errors between metamodels relative to SVR

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In