mardi 31 jan 2017
Workshop on Multiscale methods for stochastic dynamics
1Stochastic parameterizations of deterministic dynamical systems: Theory,...
2Ergodic Stochastic Differential Equations and Sampling: A numerical analysis...
3Weak convergence for semi-linear SPDEs.
4On stochastic numerical methods for the approximative pricing of financial...
5Mean-square stability analysis of SPDE approximations.
6Adaptive timestepping for S(P)DEs to control growth.
7Noise-induced transitions and mean field limits for multiscale diffusions.
8Accelerated dynamics and transition state theory.
9Long-time homogenization of the wave equation.
1Stochastic parameterizations of deterministic dynamical systems: Theory,...43:26
2Ergodic Stochastic Differential Equations and Sampling: A numerical analysis...44:28
3Weak convergence for semi-linear SPDEs.43:09
4On stochastic numerical methods for the approximative pricing of financial...41:17
5Mean-square stability analysis of SPDE approximations.38:52
6Adaptive timestepping for S(P)DEs to control growth.44:43
7Noise-induced transitions and mean field limits for multiscale diffusions.44:20
8Accelerated dynamics and transition state theory.45:59
9Long-time homogenization of the wave equation.42:13
Joint work with Arnulf Jentzen, Ryan Kurniawan, and Timo Welti.
In numerical analysis for stochastic differential equations, a general rule of thumb is that the optimal weak convergence rate of a numerical scheme is twice the optimal strong convergence rate. However, for SPDEs the optimal weak convergence rate is difficult to establish theoretically. Recently, progress was been made by Jentzen, Kurniawan and Welti for semi-linear SPDEs using the so-called mild Itô formula. We consider this approach for wave equations.