Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: He, Xu; Tuo, Rui; Wu, C. F. Jeff
• Is Part Of: Technometrics. Volume 59:Issue 1(2017)
• Page Start: 58
• Page End: 68
• Publication Date: 2017-01-02
• Subjects: Design of experiment -- Expected improvement criterion -- Gaussian process model -- Kriging -- Multi-fidelity experiment; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2016.1142902
• Languages: English
• ISSNs: 0040-1706

Abstract

Abstract : Computer experiments based on mathematical models are powerful tools for understanding physical processes. This article addresses the problem of kriging-based optimization for deterministic computer experiments with tunable accuracy. Our approach is to use multi-fidelity computer experiments with increasing accuracy levels and a nonstationary Gaussian process model. We propose an optimization scheme that sequentially adds new computer runs by following two criteria. The first criterion, called EQI, scores candidate inputs with given level of accuracy, and the second criterion, called EQIE, scores candidate combinations of inputs and accuracy. From simulation results and a real example using finite element analysis, our method outperforms the expected improvement (EI) criterion that works for single-accuracy experiments. Supplementary materials for this article are available online.