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Title: Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion

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- delity 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. Here, from simulation results and a real example using finite element analysis, our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.
Authors:
 [1] ;  [1] ;  [2]
  1. Chinese Academy of Sciences (CAS), Beijing (China)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Report Number(s):
DOE-GT-0010548-7
Journal ID: ISSN 0040-1706; FG02-13ER26159
Grant/Contract Number:
SC0010548
Type:
Accepted Manuscript
Journal Name:
Technometrics
Additional Journal Information:
Journal Volume: 59; Journal Issue: 1; Journal ID: ISSN 0040-1706
Publisher:
Taylor & Francis
Research Org:
Georgia Tech Research Corp., Atlanta, GA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Design of experiment; Expected improvement criterion; Gaussian process model; Kriging; Multi-fidelity experiment
OSTI Identifier:
1405181

He, Xu, Tuo, Rui, and Jeff Wu, C. F.. Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. United States: N. p., Web. doi:10.1080/00401706.2016.1142902.
He, Xu, Tuo, Rui, & Jeff Wu, C. F.. Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. United States. doi:10.1080/00401706.2016.1142902.
He, Xu, Tuo, Rui, and Jeff Wu, C. F.. 2017. "Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion". United States. doi:10.1080/00401706.2016.1142902. https://www.osti.gov/servlets/purl/1405181.
@article{osti_1405181,
title = {Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion},
author = {He, Xu and Tuo, Rui and Jeff Wu, C. F.},
abstractNote = {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- delity 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. Here, from simulation results and a real example using finite element analysis, our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.},
doi = {10.1080/00401706.2016.1142902},
journal = {Technometrics},
number = 1,
volume = 59,
place = {United States},
year = {2017},
month = {1}
}