Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140301
Title: A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design
Authors: Liu, Haitao
Ong, Yew-Soon
Cai, Jianfei
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Liu, H., Ong, Y.-S, & Cai, J. (2018). A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design. Structural and Multidisciplinary Optimization, 57(1), 393-416. doi:10.1007/s00158-017-1739-8
Journal: Structural and Multidisciplinary Optimization
Abstract: Metamodeling is becoming a rather popular means to approximate the expensive simulations in today’s complex engineering design problems since accurate metamodels can bring in a lot of benefits. The metamodel accuracy, however, heavily depends on the locations of the observed points. Adaptive sampling, as its name suggests, places more points in regions of interest by learning the information from previous data and metamodels. Consequently, compared to traditional space-filling sampling approaches, adaptive sampling has great potential to build more accurate metamodels with fewer points (simulations), thereby gaining increasing attention and interest by both practitioners and academicians in various fields. Noticing that there is a lack of reviews on adaptive sampling for global metamodeling in the literature, which is needed, this article categorizes, reviews, and analyzes the state-of-the-art single−/multi-response adaptive sampling approaches for global metamodeling in support of simulation-based engineering design. In addition, we also review and discuss some important issues that affect the success of an adaptive sampling approach as well as providing brief remarks on adaptive sampling for other purposes. Last, challenges and future research directions are provided and discussed.
URI: https://hdl.handle.net/10356/140301
ISSN: 1615-147X
DOI: 10.1007/s00158-017-1739-8
Rights: © 2017 Springer-Verlag GmbH Germany. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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