Lifted Brownian Kriging Models

Matthew Plumlee*, Daniel W. Apley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Gaussian processes have become a standard framework for modeling deterministic computer simulations and producing predictions of the response surface. This article investigates a new covariance function that is shown to offer superior prediction compared to the more common covariances for computer simulations of real physical systems. This is demonstrated via a gamut of realistic examples. A simple, closed-form expression for the covariance is derived as a limiting form of a Brownian-like covariance model as it is extended to some hypothetical higher-dimensional input domain, and so we term it a lifted Brownian covariance. This covariance has connections with the multiquadric kernel. Through analysis of the kriging model, this article offers some theoretical comparisons between the proposed covariance model and existing covariance models. The major emphasis of the theory is explaining why the proposed covariance is superior to its traditional counterparts for many computer simulations of real physical systems. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)165-177
Number of pages13
Issue number2
StatePublished - Apr 3 2017


  • Computer experiments
  • Gaussian process
  • Metamodeling
  • Multiquadric
  • Response surface modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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