Illinois Institute of Technology, Chicago, August 2010
Stochastic partial differential equations are appropriate models for many randomly influenced systems. In order to quantify uncertainty and estimate predictability of these systems, numerical approximations of stochastic partial differential equations are crucial.
With Peter E. Kloeden as the Principal Lecturer, this conference will focus on new advances and possible future research directions in numerical approaches for simulating and analyzing stochastic partial differential equations, with applications for understanding uncertainty quantification, noise-induced phenomena and predictability.
