The Multiple-Update-Infill Sampling Method Using Minimum Energy Design for Sequential Surrogate Modeling

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Computer experiments are widely used to evaluate the performance and reliability of engineering systems with the lowest possible time and cost. Sometimes, a high-fidelity model is required to ensure predictive accuracy; this becomes computationally intensive when many computational analyses are required (for example, inverse analysis or uncertainty analysis). In this context, a surrogate model can play a valuable role in addressing computational issues. Surrogate models are fast approximations of high-fidelity models. One efficient way for surrogate modeling is the sequential sampling (SS) method. The SS method sequentially adds samples to refine the surrogate model. This paper proposes a multiple-update-infill sampling method using a minimum energy design to improve the global quality of the surrogate model. The minimum energy design was recently developed for global optimization to find multiple optima. The proposed method was evaluated with other multiple-update-infill sampling methods in terms of convergence, accuracy, sampling efficiency, and computational cost.
Publisher
MDPI
Issue Date
2018-04
Language
English
Article Type
Article
Keywords

COMPUTER EXPERIMENTS; GLOBAL OPTIMIZATION; LATIN HYPERCUBES; PREDICTION; SURFACES; CRITERIA

Citation

APPLIED SCIENCES-BASEL, v.8, no.4

ISSN
2076-3417
DOI
10.3390/app8040481
URI
http://hdl.handle.net/10203/244068
Appears in Collection
CE-Journal Papers(저널논문)
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