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Adaptive timestepping for S(P)DEs to control growth.

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Gabriel Lord
mercredi, 1 février 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.
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Collection

Workshop on Multiscale methods for stochastic dynamics

1

Stochastic parameterizations of deterministic dynamical systems: Theory, applications and challenges

Georg Gottwald
mardi 31 janvier 2017
2

Ergodic Stochastic Differential Equations and Sampling: A numerical analysis perspective

Kostas Zygalakis
mardi 31 janvier 2017
3

Weak convergence for semi-linear SPDEs.

Sonja Cox
mardi 31 janvier 2017
4

On stochastic numerical methods for the approximative pricing of financial derivatives.

Arnulf Jentzen
mardi 31 janvier 2017
5

Mean-square stability analysis of SPDE approximations.

Annika Lang
mercredi 1 février 2017
6

Adaptive timestepping for S(P)DEs to control growth.

Gabriel Lord
mercredi 1 février 2017
7

Noise-induced transitions and mean field limits for multiscale diffusions.

Greg Pavliotis
mercredi 1 février 2017
8

Accelerated dynamics and transition state theory.

Tony Lelièvre
mercredi 1 février 2017
9

Long-time homogenization of the wave equation.

Antoine Gloria
mercredi 1 février 2017