Sequential projection maximin distance sampling method
- Authors
- Jang, J; Lim, W; Cho, S; Lee, M; Na, J; Lee, Tae Hee
- Issue Date
- Dec-2013
- Publisher
- APCOM2013 & ISCM2013
- Keywords
- Design of experiment (DOE); Sequential design; Maximin distance design; Space filling design; Projective property; Surrogate model
- Citation
- Proceedings of the 5th Asia Pacific Congress on Computational Mechanics and 4th International Symposium on Computational Mechanics, pp.1 - 8
- Indexed
- OTHER
- Journal Title
- Proceedings of the 5th Asia Pacific Congress on Computational Mechanics and 4th International Symposium on Computational Mechanics
- Start Page
- 1
- End Page
- 8
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/161089
- Abstract
- Design optimization for engineering problems often requires severe computer simulations. Thus, to perform a design optimization efficiently, surrogate models replacing the time-consuming simulator by using the adequate number of computer experiments, i.e., design and analysis of computer experiments (DACE) have been developed. Our goal in this paper is to propose a sequential design of experiments to construct a global surrogate model. The proposed method employs the priority of variables defined from non-linearity, contribution ratio or global sensitivity. The
priority gives a chance to have better projective property to more important variable, because relatively more important variable significantly influences on the accuracy of surrogate model. Consequently this causes a decrease in the error of surrogate model and a reduction of the total number of sample points. The proposed method is compared with sequential maximin distance design and optimal Latin hypercube design with two examples.
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