The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Review of Metamodeling Techniques in Support of Engineering Design Optimization
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
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
Cited by
Use of Kriging Models to Approximate Deterministic Computer Models
Martin, J. D., & Simpson, T. W. (2005) · AIAA Journal · doi:10.2514/1.8650 · Cited by 835
The evolution, challenges, and future of knowledge representation in product design systems
Chandrasegaran, S. K., Ramani, K., Sriram, R. D., Horváth, I., Bernard, A., Harik, R. F., & Gao, W. (2013) · Computer-Aided Design · doi:10.1016/j.cad.2012.08.006 · Cited by 667
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
Han, Z.-H., & Görtz, S. (2012) · AIAA Journal · doi:10.2514/1.j051354 · Cited by 490
A review of machine learning for the optimization of production processes
Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03988-5 · Cited by 363
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Metadata
- Authors
- Wang, G. G., & Shan, S.
- Venue
- Journal of Mechanical Design
- Publisher
- ASME International
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Review of Metamodeling Techniques in Support of Engineering Design Optimization. https://app.cochat.ai/papers/10.1115/1.2429697
- The paper
- 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
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.