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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Uncertainty quantification for partial differential equations: going beyond Monte Carlo

## Uncertainty quantification for partial differential equations: going beyond Monte CarloAdd to your list(s) Download to your calendar using vCal - Max Gunzburger (Florida State University)
- Tuesday 09 January 2018, 10:00-11:00
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. UNQW01 - Key UQ methodologies and motivating applications We consider the determination of statistical information about outputs of interest that depend on the solution of a partial differential equation having random inputs, e.g., coefficients, boundary data, source term, etc. Monte Carlo methods are the most used approach used for this purpose. We discuss other approaches that, in some settings, incur far less computational costs. These include quasi-Monte Carlo, polynomial chaos, stochastic collocation, compressed sensing, reduced-order modeling, and multi-level and multi-fidelity methods for all of which we also discuss their relative strengths and weaknesses. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
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