Presented by:
Max Gunzburger Florida State University
Date:
Tuesday 9th January 2018 - 10:00 to 11:00
Venue:
INI Seminar Room 1
Abstract:
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.
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