International audienceIn this work, we adapt the micro-macro methodology to stochastic differential equations for the purpose of numerically solving oscillatory evolution equations. The models we consider are addressed in a wide spectrum of regimes where oscillations may be slow or fast. We show that through an ad-hoc transformation (the micro-macro decomposition), it is possible to retain the usual orders of convergence of Euler-Maruyama method, that is to say, uniform weak order one and uniform strong order one half
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...
International audienceIn the analysis of highly-oscillatory evolution problems, it is commonly assum...
35 pages, 6 figures. Submitted to Annals of Applied ProbabilityInternational audienceWe propose an a...
In this work, we adapt the {\em micro-macro} methodology to stochastic differential equations for th...
International audienceIn this work, we adapt the micro-macro methodology to stochastic differential ...
International audienceWe introduce a class of numerical methods for highly oscillatory systems of st...
International audienceWe introduce a new methodology to design uniformly accurate methods for oscil-...
We introduce a new methodology to design uniformly accurate methods for oscillatory evolution equati...
We study the strong convergence order of the Euler-Maruyama scheme for scalar stochastic differentia...
We consider linear multi-step methods for stochastic ordinary differential equations and study their...
We study a class of numerical methods for a system of second-order SDE driven by a linear fast force...
In this work, we generalize the current theory of strong convergence rates for the backward Euler–Ma...
We prove strong convergence of order 1/4 - E for arbitrarily small E > 0 of the Euler-Maruyama meth...
In this article, we construct and analyse an explicit numerical splitting method for a class of semi...
We study a family of numerical schemes applied to a class of multiscale systems of stochastic differ...
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...
International audienceIn the analysis of highly-oscillatory evolution problems, it is commonly assum...
35 pages, 6 figures. Submitted to Annals of Applied ProbabilityInternational audienceWe propose an a...
In this work, we adapt the {\em micro-macro} methodology to stochastic differential equations for th...
International audienceIn this work, we adapt the micro-macro methodology to stochastic differential ...
International audienceWe introduce a class of numerical methods for highly oscillatory systems of st...
International audienceWe introduce a new methodology to design uniformly accurate methods for oscil-...
We introduce a new methodology to design uniformly accurate methods for oscillatory evolution equati...
We study the strong convergence order of the Euler-Maruyama scheme for scalar stochastic differentia...
We consider linear multi-step methods for stochastic ordinary differential equations and study their...
We study a class of numerical methods for a system of second-order SDE driven by a linear fast force...
In this work, we generalize the current theory of strong convergence rates for the backward Euler–Ma...
We prove strong convergence of order 1/4 - E for arbitrarily small E > 0 of the Euler-Maruyama meth...
In this article, we construct and analyse an explicit numerical splitting method for a class of semi...
We study a family of numerical schemes applied to a class of multiscale systems of stochastic differ...
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...
International audienceIn the analysis of highly-oscillatory evolution problems, it is commonly assum...
35 pages, 6 figures. Submitted to Annals of Applied ProbabilityInternational audienceWe propose an a...