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Designs for Computer Experiments
Abstract
A computer experiment generates observations by running a computer model at inputs x and recording the output (response) Y. Prediction of the response Y to an untried input is treated by modeling the systematic departure of Y from a linear model as a realization of a stochastic process. For given data (selected inputs and the computed responses), best linear prediction is used. The design problem is to select the inputs to predict efficiently. The issues of choice of stochastic-process model and computation of efficient designs are addressed, and applications are made to some chemical kinetics problems.
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Citations
Cited by
Factorial Sampling Plans for Preliminary Computational Experiments
Morris, M. D. (1991) · Technometrics · doi:10.1080/00401706.1991.10484804 · Cited by 4,943
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Minimax and maximin distance designs
Johnson, M. E., Moore, L. M., & Ylvisaker, D. (1990) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(90)90122-b · Cited by 1,451
Sequential kriging optimization using multiple-fidelity evaluations
Huang, D., Allen, T. T., Notz, W. I., & Miller, R. A. (2006) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-005-0587-0 · Cited by 414
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Metadata
- Authors
- Sacks, J., Schiller, S. B., & Welch, W. J.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1989-02
- Volume
- 31
- Issue
- 1
- Pages
- 41-47
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2044458183
- Cited by
- 970
- References
- 0
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sacks, J., Schiller, S. B., & Welch, W. J. (1989). Designs for Computer Experiments. Technometrics. https://doi.org/10.1080/00401706.1989.10488474
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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.
