The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
A review on design, modeling and applications of computer experiments
Abstract
In this paper, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review first presents several engineering applications, it then describes approaches for the two primary tasks of metamodeling: (i) selecting an experimental design; and (ii) fitting a statistical model. Seven statistical modeling methods are included. Both classical and newer experimental designs are discussed. Finally, our own computational study tests the various metamodeling options on two two-dimensional response surfaces and one ten-dimensional surface.
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Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,630
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053
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
Cited by
Nazir, A., Abate, K. M., Kumar, A., & Jeng, J.-Y. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04085-3 · Cited by 528
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Van Gelder, L., Das, P., Janssen, H., & Roels, S. (2014) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2014.10.004 · Cited by 123
Vu, H. T., Scarlett, C. J., & Vuong, Q. V. (2019) · Journal of Food Science and Technology · doi:10.1007/s13197-019-03610-2 · Cited by 90
Metadata
- Authors
- Chen, V. C. P., Tsui, K.-L., Barton, R. R., & Meckesheimer, M.
- Venue
- IIE Transactions
- Publisher
- Informa UK Limited
- Published
- 2006-04
- Volume
- 38
- Issue
- 4
- Pages
- 273-291
- ISSN
- 0740-817X (ISSN-L)
- 1545-8830
- OpenAlex
- W2081709852
- Cited by
- 317
- References
- 98
- FWCI
- 20.63
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
