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Adaptive timestepping for S(P)DEs to control growth.
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Wednesday, 1 February 2017
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Faculté des sciences -
Section de mathématiques
We introduce a class of adaptive timestepping strategies for stochastic differential equations such as those arising from the semi-discretization of SPDEs with non-Lipschitz drift coefficients. These strategies work by controlling potential unbounded growth in solutions of a numerical scheme. We prove that the Euler-Maruyama scheme with an adaptive timestepping strategy in this class is strongly convergent and present preliminary results on a semi-implicit scheme and an extension to non-Lipschitz noise terms. We test this alternative to taming on some examples. This is joint work with Conall Kelly.
Collection
Workshop on Multiscale methods for stochastic dynamics
1
Stochastic parameterizations of deterministic dynamical systems: Theory, applications and challenges
Georg Gottwald
Tuesday 31 January 2017
2
Ergodic Stochastic Differential Equations and Sampling: A numerical analysis perspective
Kostas Zygalakis
Tuesday 31 January 2017
3
Weak convergence for semi-linear SPDEs.
Sonja Cox
Tuesday 31 January 2017
4
On stochastic numerical methods for the approximative pricing of financial derivatives.
Arnulf Jentzen
Tuesday 31 January 2017
5
Mean-square stability analysis of SPDE approximations.
Annika Lang
Wednesday 1 February 2017
6
Adaptive timestepping for S(P)DEs to control growth.
Gabriel Lord
Wednesday 1 February 2017
7
Noise-induced transitions and mean field limits for multiscale diffusions.
Greg Pavliotis
Wednesday 1 February 2017
8
Accelerated dynamics and transition state theory.
Tony Lelièvre
Wednesday 1 February 2017
9
Long-time homogenization of the wave equation.
Antoine Gloria
Wednesday 1 February 2017