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SUMMARY:Uncertainty quantification for partial differential equations: goi
 ng beyond Monte Carlo - Max Gunzburger (Florida State University)
DTSTART:20180109T100000Z
DTEND:20180109T110000Z
UID:TALK97474@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION: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\, sou
 rce term\, etc. Monte Carlo methods are the most used approach used for th
 is purpose. We discuss other approaches that\, in some settings\, incur fa
 r less computational costs. These include quasi-Monte Carlo\, polynomial c
 haos\, stochastic collocation\, compressed sensing\, reduced-order modelin
 g\, and multi-level and multi-fidelity methods for all of which we also di
 scuss their relative strengths and weaknesses.
LOCATION:Seminar Room 1\, Newton Institute
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