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Bayesian Metamodeling for Computer Experiments Using the Gaussian Kriging Models
Author(s) -
Deng Haisong,
Shao Wenze,
Ma Yizhong,
Wei Zhuihui
Publication year - 2012
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1259
Subject(s) - kriging , metamodeling , computer experiment , gaussian process , computer science , bayesian inference , bayesian probability , benchmark (surveying) , inference , process (computing) , mathematical optimization , gaussian , data mining , machine learning , artificial intelligence , mathematics , simulation , physics , operating system , geodesy , quantum mechanics , programming language , geography
In the past two decades, more and more quality and reliability activities have been moving into the design of product and process. The design and analysis of computer experiments, as a new frontier of the design of experiments, has become increasingly popular among modern companies for optimizing product and process conditions and producing high‐quality yet low‐cost products and processes. This article mainly focuses on the issue of constructing cheap metamodels as alternatives to the expensive computer simulators and proposes a new metamodeling method on the basis of the Gaussian stochastic process model or Gaussian Kriging. Rather than a constant mean as in ordinary Kriging or a fixed mean function as in universal Kriging, the new method captures the overall trend of the performance characteristics of products and processes through a more accurate mean, by efficiently incorporating a scheme of sparseness prior–based Bayesian inference into Kriging. Meanwhile, the mean model is able to adaptively exclude the unimportant effects that deteriorate the prediction performance. The results of an experiment on empirical applications demonstrate that, compared with several benchmark methods in the literature, the proposed Bayesian method is not only much more effective in approximation but also very efficient in implementation, hence more appropriate than the widely used ordinary Kriging to empirical applications in the real world. Copyright © 2011 John Wiley & Sons, Ltd.