Gradient-Enhanced Universal Kriging for Uncertainty Propagation

TitleGradient-Enhanced Universal Kriging for Uncertainty Propagation
Publication TypeJournal Article
Year of Publication2010
AuthorsLockwood, BA, Anitescu, M
Date Published11/2010
Other NumbersANL/MCS-P1808-1110
Abstract

<p>In this work, we investigate the issue of providing a statistical model for the response of a computer model-described nuclear engineering system, for use in uncertainty propagation. The motivation behind our approach is the need for providing an uncertainty assessment even in the circumstances where only a few samples are available. Building on our recent work in using a regression approach with derivative information for approximating the system response, we investigate the ability of a universal gradient- enhanced Kriging model to provide a means for inexpensive uncertainty quantication. The universal Kriging model can be viewed as a hybrid of polynomial regression and Gaussian process regression. For this model, the mean behavior of the surrogate is determined by a polynomial regression, and deviations from this mean are represented as a Gaussian process. Tests with explicit functions and nuclear engineering models show that the universal gradient-enhanced Kriging model provides a more accurate surrogate model when compared to either regression or ordinary Kriging models. In addition we investigate the ability of the Kriging model to provide error predictions and bounds for regression models.</p>

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