Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function

Yongxiang Li, Qiang Zhou

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Gaussian process (GP) is a popular method for emulating deterministic computer simulation models. Its natural extension to computer models with multivariate outputs employs a multivariate Gaussian process (MGP) framework. Nevertheless, with significant increase in the number of design points and the number of model parameters, building an MGP model is a very challenging task. Under a general MGP model framework with nonseparable covariance functions, we propose an efficient meta-modeling approach featuring a pairwise model building scheme. The proposed method has excellent scalability even for a large number of output levels. Some properties of the proposed method have been investigated and its performance has been demonstrated through several numerical examples. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)483-494
Number of pages12
JournalTechnometrics
Volume58
Issue number4
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Keywords

  • Computer experiment
  • Meta-models
  • Multivariate Gaussian process
  • Pairwise modeling
  • Pseudolikelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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