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International Journal for Uncertainty Quantification
IF: 0.967 5-Year IF: 1.301

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2011003369
pages 25-38


Chao Yang
School of Computing, University of Utah, Salt Lake City, UT 84112, USA
Dongbin Xiu
Ohio State University
Mike Kirby
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA


We present a numerical technique to visualize covariance and cross-covariance fields of a stochastic simulation. The method is local in the sense that it demonstrates the covariance structure of the solution at a point with its neighboring locations. When coupled with an efficient stochastic simulation solver, our framework allows one to effectively concurrently visualize both the mean and (cross-)covariance information for two-dimensional (spatial) simulation results. Most importantly, the visualization provides the scientist a means to identify the interesting correlation structure of the solution field. The mathematical setup is discussed, along with several examples to demonstrate the efficacy of this approach.