ADAPTIVE METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS VIA NATURAL EMBEDDINGS AND REJECTION SAMPLING WITH MEMORY.

Christopher Rackauckas1, Qing Nie1

  • 1Department of Mathematics, Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.

Discrete and Continuous Dynamical Systems. Series B
|March 13, 2018
PubMed
Summary

New adaptive methods for stochastic differential equations (SDEs) use embedded stochastic Runge-Kutta (SRK) pairs and rejection sampling with memory (RSwM) for efficient, accurate solutions. These methods significantly outperform fixed-timestep approaches on complex models.

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