Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118105
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Type: Journal article
Title: A gradient-enhanced sparse grid algorithm for uncertainty quantification
Author: de Baar, J.
Harding, B.
Citation: International Journal for Uncertainty Quantification, 2015; 5(5):453-468
Publisher: Begell House
Issue Date: 2015
ISSN: 2152-5080
2152-5099
Statement of
Responsibility: 
Jouke H. S. de Baar and Brendan Harding
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.
Keywords: High-dimensional surrogates; uncertainty quantification; sparse grids
Rights: © 2015 by Begell House, Inc. 453
RMID: 0030106414
DOI: 10.1615/Int.J.UncertaintyQuantification.2015014394
Appears in Collections:Medicine publications

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