Multi-Index Monte Carlo methods for PDEs with random coefficients

Josef Dick (UNSW, Sidney)

May 04. 2020, 00:00 — 00:35

Approximating the expected value of a PDE with random coefficients is computationally expensive. Various strategies have been developed which improve convergence rates and computational run times of algorithms. In this presentation we discuss  the Multi-Index Monte Carlo method applied to a standard diffusion problem with random coefficients.

Further Information
Erwin Schrödinger Institute - virtual
Associated Event:
Multilevel and multifidelity sampling methods in UQ for PDEs (Online Workshop)
Kody Law (U Manchester)
Fabio Nobile (EPFL Lausanne)
Robert Scheichl (U Heidelberg)
Karen Willcox (U of Texas, Austin)