A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

• Main Authors: Liu, Haitao, Ong, Yew-Soon, Cai, Jianfei
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2020
• 主題: Engineering::Computer science and engineering; Adaptive Sampling; Global Metamodeling
• 在線閱讀: https://hdl.handle.net/10356/140301

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.