Numerical Methods for Stochastic Obstacle Problems
The Faculty of Informatics is pleased to announce a seminar given by Alexey Chernov
DATE: Wednesday, November 7th, 2012
PLACE: University of Lugano, room CC354, Main building (Via G. Buffi 13)
Obstacle problems arise in many fields in science and engineering. The main difficulty in these problems is their specific nonlinear character and the limited global regularity of the solution caused by the presence of inequality constraints. Nonetheless, combined with the state-of-the-art hardware, advanced numerical schemes are capable to produce a highly accurate and efficient deterministic numerical simulation, provided the problem data are known exactly.
However, in real applications, the complete knowledge of the problem parameters is not realistic. One way to treat the lack of knowledge is to model uncertain parameters as random fields.
In this talk we review some recent advances in numerical analysis and simulation for a class of stochastic obstacle problems. In particular, we discuss Stochastic Galerkin/Collocation and the Multilevel Monte Carlo approaches, present the asymptotic error estimates and illustrate the numerical performance of the above mentioned methods on several model problems.
Joint work with Claudio Bierig, University of Bonn.
Alexey Chernov is professor at the Hausdorff Center for Mathematics and the Institute for Numerical Simulation at the University of Bonn, Germany. He received a diploma in mathematics (with honors) from Lomonosov Moscow State University, Russia, in 2003 and a PhD from the University of Hannover, Germany, in 2006. Subsequently he held post-doctoral positions at the University of Hannover and ETH Zuerich, Switzerland. His research concentrates on construction, analysis and efficient implementation of numerical methods for partial differential equations and integral equations.
HOST: Prof. Rolf Krause