Leveraging the nugget parameter for efficient Gaussian process modeling

Ramin Bostanabad, Tucker Kearney, Siyu Tao, Daniel W. Apley, Wei Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Gaussian process (GP) metamodels have been widely used as surrogates for computer simulations or physical experiments. The heart of GP modeling lies in optimizing the log-likelihood function with respect to the hyperparameters to fit the model to a set of observations. The complexity of the log-likelihood function, computational expense, and numerical instabilities challenge this process. These issues limit the applicability of GP models more when the size of the training data set and/or problem dimensionality increase. To address these issues, we develop a novel approach for fitting GP models that significantly improves computational expense and prediction accuracy. Our approach leverages the smoothing effect of the nugget parameter on the log-likelihood profile to track the evolution of the optimal hyperparameter estimates as the nugget parameter is adaptively varied. The new approach is implemented in the R package GPM and compared to a popular GP modeling R package (GPfit) for a set of benchmark problems. The effectiveness of the approach is also demonstrated using an engineering problem to learn the constitutive law of a hyperelastic composite where the required level of accuracy in estimating the response gradient necessitates a large training data set.

Original languageEnglish (US)
Pages (from-to)501-516
Number of pages16
JournalInternational Journal for Numerical Methods in Engineering
Volume114
Issue number5
DOIs
StatePublished - May 4 2018

Keywords

  • Gaussian process
  • computer experiments
  • hyperparameter estimation
  • ill-conditioned matrix
  • nugget parameter

ASJC Scopus subject areas

  • Numerical Analysis
  • Engineering(all)
  • Applied Mathematics

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