The area of Uncertainty Quantification (UQ) is growing in importance for DOE and computational science in general. Within LBNL, we have an excellent mix of skills in mathematics, modeling, parallel programming, and systems to address this challenge head-on. But we currently have no coherent approach to UQ, in part because the problem space is large and ill-defined: a recent DOE panel report defines UQ as "the end-to-end study of the reliability of scientific inferences", which would seem to include the entire field of statistics. Others, such as the Stanford UQ group, define it more narrowly in the context of CFD. Which is right for DOE exascale computing, and why?
The purpose of this summer reading group is to familiarize participants with the varied aspects of the UQ challenge, and spur discussions of how LBNL research could help address these. This will be an opportunity to cross-germinate ideas and explore collaborations across groups.
For more information, see the Schedule.
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