skip to content
 

Uncertainty quantification for partial differential equations: going beyond Monte Carlo

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.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons