期刊
TECHNOMETRICS
卷 55, 期 1, 页码 47-+出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/00401706.2012.715835
关键词
Computer experiment; Convolved process; Coregionalization; Metamodel
资金
- Research Councils UK
The Gaussian process regression model is a popular type of emulator used as a fast surrogate for computationally expensive simulators (deterministic computer models). For simulators with multivariate output, common practice is to specify a separable covariance structure for the Gaussian process. Though computationally convenient, this can be too restrictive, leading to poor performance of the emulator, particularly when the different simulator outputs represent different physical quantities. Also, treating the simulator outputs as independent can lead to inappropriate representations of joint uncertainty. We develop nonseparable covariance structures for Gaussian process emulators, based on the linear model of coregionalization and convolution methods. Using two case studies, we compare the performance of these covariance structures both with standard separable covariance structures and with emulators that assume independence between the outputs. In each case study, we find that only emulators with nonseparable covariances structures have sufficient flexibility both to give good predictions and to represent joint uncertainty about the simulator outputs appropriately. This article has supplementary material online.
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