Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

  • Authors:

  • Ilias Bilionis;Nicholas Zabaras;Bledar A. Konomi;Guang Lin

  • Affiliations:

  • Center for Applied Mathematics, 657 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853, USA and Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace ...;Center for Applied Mathematics, 657 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853, USA and Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace ...;Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN K7-90, Richland, WA 99352, USA;Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN K7-90, Richland, WA 99352, USA

  • Venue:
  • Journal of Computational Physics
  • Year:
  • 2013

Quantified Score

Hi-index 31.45

Visualization

Abstract

Computer codes simulating physical systems usually have responses that consist of a set of distinct outputs (e.g., velocity and pressure) that evolve also in space and time and depend on many unknown input parameters (e.g., physical constants, initial/boundary conditions, etc.). Furthermore, essential engineering procedures such as uncertainty quantification, inverse problems or design are notoriously difficult to carry out mostly due to the limited simulations available. The aim of this work is to introduce a fully Bayesian approach for treating these problems which accounts for the uncertainty induced by the finite number of observations. Our model is built on a multi-dimensional Gaussian process that explicitly treats correlations between distinct output variables as well as space and/or time. The proper use of a separable covariance function enables us to describe the huge covariance matrix as a Kronecker product of smaller matrices leading to efficient algorithms for carrying out inference and predictions. The novelty of this work, is the recognition that the Gaussian process model defines a posterior probability measure on the function space of possible surrogates for the computer code and the derivation of an algorithmic procedure that allows us to sample it efficiently. We demonstrate how the scheme can be used in uncertainty quantification tasks in order to obtain error bars for the statistics of interest that account for the finite number of observations.