Stochastic ordinary differential equations in applied and computational mathematics

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Using concrete examples, we discuss the current and potential use of stochastic ordinary differential equations (SDEs) from the perspective of applied and computational mathematics. Assuming only a minimal background knowledge in probability and stochastic processes, we focus on aspects that distinguish SDEs from their deterministic counterparts. To illustrate a multiscale modelling framework, we explain how SDEs arise naturally as diffusion limits in the type of discrete-valued stochastic models used in chemical kinetics, population dynamics, and, most topically, systems biology. We outline some key issues in existence, uniqueness and stability that arise when SDEs are used as physical models, and point out possible pitfalls. We also discuss the use of numerical methods to simulate trajectories of an SDE and explain how both weak and strong convergence properties are relevant for highly-efficient multilevel Monte Carlo simulations. We flag up what we believe to be key topics for future research, focussing especially on nonlinear models, parameter estimation, model comparison and multiscale simulation.
Original languageEnglish
Pages (from-to)449-474
Number of pages26
JournalIMA Journal of Applied Mathematics
Issue number3
Publication statusPublished - Jun 2011

Keywords / Materials (for Non-textual outputs)

  • differential equations
  • computational mathematics
  • trajectories


Dive into the research topics of 'Stochastic ordinary differential equations in applied and computational mathematics'. Together they form a unique fingerprint.

Cite this