The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Multi-fidelity optimization via surrogate modelling
Abstract
This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging . An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an intuitive manner, along with a new variance estimator to account for varying degrees of computational ‘noise’ in the multiple levels of analysis. A multi-fidelity wing optimization is used to demonstrate the methodology.
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Cites
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Factorial Sampling Plans for Preliminary Computational Experiments
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McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489755 · Cited by 3,220
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A statistical approach to some basic mine valuation problems on the Witwatersrand
Krige, D. G. (1951) · Journal of the Southern African Institute of Mining and Metallurgy · Cited by 2,409
A Taxonomy of Global Optimization Methods Based on Response Surfaces
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Predicting the output from a complex computer code when fast approximations are available
Kennedy, M. (2000) · Biometrika · doi:10.1093/biomet/87.1.1 · Cited by 1,605
Exploratory designs for computational experiments
Morris, M. D., & Mitchell, T. J. (1995) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(94)00035-t · Cited by 1,197
A Comparison of Algorithms for Constructing Exact D-Optimal Designs
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Cited by
Recent advances in surrogate-based optimization
Forrester, A. I. J., & Keane, A. J. (2009) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2008.11.001 · Cited by 2,357
Self-citationEngineering Design via Surrogate Modelling: A Practical Guide
Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008) · doi:10.1002/9780470770801 · Cited by 2,309
Self-citationMachine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
Han, Z.-H., & Görtz, S. (2012) · AIAA Journal · doi:10.2514/1.j051354 · Cited by 490
Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
Zhang, X., Xie, F., Ji, T., Zhu, Z., & Zheng, Y. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113485 · Cited by 380
Metadata
- Authors
- Forrester, A. I. J., Sóbester, A., & Keane, A. J.
- Venue
- Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2007-10-02
- Volume
- 463
- Issue
- 2088
- Pages
- 3251-3269
- ISSN
- 1364-5021 (ISSN-L)
- 1471-2946
- OpenAlex
- W1982886636
- Cited by
- 1,018
- References
- 15
- FWCI
- 6.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2007). Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rspa.2007.1900
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
