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

Journal Article · · Technometrics
 [1];  [1];  [2]
  1. Chinese Academy of Sciences (CAS), Beijing (China)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
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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.

Research Organization:
Georgia Institute of Technology, Atlanta, GA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0010548
OSTI ID:
1405181
Report Number(s):
DOE-GT-0010548-7; FG02-13ER26159
Journal Information:
Technometrics, Vol. 59, Issue 1; ISSN 0040-1706
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

References (23)

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Multi-fidelity optimization via surrogate modelling
  • Forrester, Alexander I. J.; Sóbester, András; Keane, Andy J.
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 463, Issue 2088 https://doi.org/10.1098/rspa.2007.1900
journal September 2007
The Design and Analysis of Computer Experiments book January 2003
A literature survey of benchmark functions for global optimisation problems journal January 2013
Design and analysis of computer experiments journal December 2010
Orthogonal Array-Based Latin Hypercubes journal December 1993
Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision text January 2013
The Design and Analysis of Computer Experiments book January 2018
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Efficient Global Optimization of Expensive Black-Box Functions journal January 1998
Nested Latin hypercube designs journal September 2009
Nested orthogonal array-based Latin hypercube designs journal August 2011
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models journal March 2006
Design and Analysis of Computer Experiments journal November 1989
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations text January 2013
Choosing the Sample Size of a Computer Experiment: A Practical Guide journal November 2009
Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision journal July 2012
Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments journal May 2008
Predicting the output from a complex computer code when fast approximations are available journal March 2000
Orthogonal Array-Based Latin Hypercubes journal December 1993
Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code journal May 1979
Integrated Analysis of Computer and Physical Experiments journal May 2004

Linked Research (3)

Figures / Tables (12)

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Related Subjects

97 MATHEMATICS AND COMPUTING
Design of experiment
Expected improvement criterion
Gaussian process model
Kriging
Multi-fidelity experiment