Quantification of Uncertainty in Extreme Scale Computations
A SciDAC Institute funded by the DOE Office of Science
Recent Accomplishments
Scalable Parallel Algorithms for Extreme-Scale Statistical Inverse Problems
Participants
QUEST is comprised of six participating institutions, namely: Sandia National Laboratories, Los Alamos National Laboratory, University of Southern California, University of Texas at Austin, Duke University, and the Massachusetts Institute of Technology. The group of participating investigators at these institutions has had a history of close collaboration on research projects in key elements of the uncertainty quantification landscape.
read more »UQ Methods
A central theme underlying our UQ methods is our use of a probabilistic representation of uncertain quantities, as either random variables or processes. Probability theory provides a rich mathematical structure for characterizing a random variable, e.g., by using its moments, quantiles, and/or density, by enumerating its possible values, or by describing its topological properties/geometry with respect to some Hilbertian basis.
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