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Review of Metamodeling Techniques in Support of Engineering Design Optimization

Wang, G. Gary, Shan, S.·2007·Journal of Mechanical Design

Abstract

Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.

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Metadata

Authors
Wang, G. G., & Shan, S.
Venue
Journal of Mechanical Design
Published
2006-05-04
Volume
129
Issue
4
Pages
370-380
ISSN
  • 0161-8458 (ISSN-L)
  • 1050-0472
  • 1528-9001
OpenAlex
W2048711666
Cited by
1,706
References
2
FWCI
28.36
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Review of Metamodeling Techniques in Support of Engineering Design Optimization. https://app.cochat.ai/papers/10.1115/1.2429697
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Wang, G. G., & Shan, S. (2007). Review of Metamodeling Techniques in Support of Engineering Design Optimization. Journal of Mechanical Design. https://doi.org/10.1115/1.2429697

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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.