Numerical Methods for Stochastic Simulation: When Stochastic Integration Meets Geometric Numerical Integration

Chapter

Abstract

In this paper we discuss a framework recently introduced to construct and analyze accurate stochastic integrators for the computation of expectation of functionals of a stochastic process for both finite time or long-time dynamics. Such accurate integrators are of interest for many applications in biology, chemistry or physics and are also often needed in multiscale stochastic simulations. We describe how ideas originating from geometric numerical integration or structure preserving methods for deterministic differential equations can help to design new integrators for weak approximation of stochastic differential equations or for long-time simulation of ergodic stochastic systems.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ANMCInstitut de Mathmatiques, École Polytechnique Fédérale de LausanneLausanneSwitzerland

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