Abstract
Multiscale Modeling & Simulation Vol. 12 No. 2 (2014) pp. 781-831 We present explicit methods for simulating diffusions whose generator is
self-adjoint with respect to a known (but possibly not normalizable) density.
These methods exploit this property and combine an optimized Runge-Kutta
algorithm with a Metropolis-Hastings Monte-Carlo scheme. The resulting
numerical integration scheme is shown to be weakly accurate at finite noise and
to gain higher order accuracy in the small noise limit. It also permits to
avoid computing explicitly certain terms in the equation, such as the
divergence of the mobility tensor, which can be tedious to calculate. Finally,
the scheme is shown to be ergodic with respect to the exact equilibrium
probability distribution of the diffusion when it exists. These results are
illustrated on several examples including a Brownian dynamics simulation of DNA
in a solvent. In this example, the proposed scheme is able to accurately
compute dynamics at time step sizes that are an order of magnitude (or more)
larger than those permitted with commonly used explicit predictor-corrector
schemes.