The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses
Abstract
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” that provides 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 present Support Vector Regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is presented, and SVR approximations are compared against the aforementioned four metamodeling techniques using a testbed of 22 engineering analysis functions. SVR achieves more accurate and more robust function approximations than these four metamodeling techniques and shows great promise for future metamodeling applications.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Gunst, R. F., Myers, R. H., & Montgomery, D. C. (1996) · Technometrics · doi:10.2307/1270613 · Cited by 9,530
Multivariate Adaptive Regression Splines
Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,000
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
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Forrester, A. I. J., & Keane, A. J. (2009) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2008.11.001 · Cited by 2,357
Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.023 · Cited by 560
Multiple surrogates: how cross-validation errors can help us to obtain the best predictor
Viana, F. A. C., Haftka, R. T., & Steffen, V. (2009) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-008-0338-0 · Cited by 420
Ding, Z., Chen, W., Hu, T., & Xu, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116660 · Cited by 85
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Liu, X., Rajan, S., Ramasarma, N., Bonato, P., & Lee, S. I. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2821136 · Cited by 39
Metadata
- Authors
- Clarke, S. M., Griebsch, J. H., & Simpson, T. W.
- Venue
- Volume 2: 29th Design Automation Conference, Parts A and B
- Publisher
- ASMEDC
- Published
- 2003-01-01
- Pages
- 535-543
- OpenAlex
- W2071204941
- Cited by
- 414
- References
- 31
- FWCI
- 4.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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