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Quelques contributions à l'analyse numérique d'équations stochastiques

Marie Kopec 1, 2
1 IPSO - Invariant Preserving SOlvers
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
Abstract : This work presents some results about behavior in long time and in finite time of numerical methods for stochastic equations. In a first part, we are considered with overdamped Langevin Stochastic Differential Equations (SDE) and Langevin SDE. We show a weak backward error analysis result for its numerical approximations defined by implicit methods. In particular, we prove that the generator associated with the numerical solution coincides with the solution of a modified Kolmogorov equation up to high order terms with respect to the stepsize. This implies that every measure of the numerical scheme is close to a modified invariant measure obtained by asymptotic expansion. Moreover, we prove that, up to negligible terms, the dynamic associated with the implicit scheme considered is exponentially mixing. In a second part, we study the long-time behavior of fully discretized semilinear SPDEs with additive space-time white noise, which admit a unique invariant probability measure μ. We focus on the discretization in time thanks to a scheme of Euler type, and on a Finite Element discretization in space and we show that the average of regular enough test functions with respect to the (possibly non unique) invariant laws of the approximations are close to the corresponding quantity for μ. More precisely, we analyze the rate of the convergence with respect to the different discretization parameters. Finally, we are concerned with semilinear SPDEs with additive space-time white noise, which the nonlinear term is a polynomial function. We analyze the rate of the weak convergence for discretization in time with an implicit splitting method.
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Submitted on : Wednesday, September 17, 2014 - 11:28:34 AM
Last modification on : Friday, May 28, 2021 - 3:37:58 AM
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  • HAL Id : tel-01064811, version 1


Marie Kopec. Quelques contributions à l'analyse numérique d'équations stochastiques. Analyse numérique [math.NA]. Ecole normale supérieure de Rennes - ENS Rennes, 2014. Français. ⟨NNT : 2014ENSR0002⟩. ⟨tel-01064811⟩



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