A gradient-enhanced sparse grid algorithm for uncertainty quantification

Jouke H.S. de Baar*, Brendan Harding

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    Adjoint-based gradient information has been successfully incorporated to create surrogate models of the output of expensive computer codes. Exploitation of these surrogates offers the possibility of uncertainty quantification, optimization and parameter estimation at reduced computational cost. Presently, when we look for a surrogate method to include gradient information, the most common choice is gradient-enhanced Kriging (GEK). As a competitor, we develop a novel method: gradient-enhanced sparse grid interpolation. Results for two test functions, the Rosenbrock function and a test function based on the drag of a transonic airfoil with random shape deformations, show that the gradient-enhanced sparse grid interpolation is a reliable surrogate that can incorporate the gradient information efficiently for high-dimensional problems.

    Original languageEnglish
    Pages (from-to)453-468
    Number of pages16
    JournalInternational Journal for Uncertainty Quantification
    Volume5
    Issue number5
    DOIs
    Publication statusPublished - 2015

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