Modified averaged vector field methods preserving multiple invariants for conservative stochastic differential equations
For researchers in numerical analysis of stochastic differential equations, this work provides a novel method to preserve multiple invariants, which is an incremental improvement over existing averaged vector field methods.
The paper proposes modified averaged vector field methods for conservative stochastic differential equations that preserve multiple invariants simultaneously, achieving mean square convergence order 1 for commutative noises. Numerical experiments confirm the theoretical results and demonstrate superiority in long-time simulations.
A novel class of conservative numerical methods for general conservative Stratonovich stochastic differential equations with multiple invariants is proposed and analyzed. These methods, which are called modified averaged vector field methods, are constructed by modifying the averaged vector field methods to preserve multiple invariants simultaneously. Based on the prior estimate for high order moments of the modification coefficient, the mean square convergence order $1$ of proposed methods is proved in the case of commutative noises. In addition, the effect of quadrature formula on the mean square convergence order and the preservation of invariants for the modified averaged vector field methods is considered. Numerical experiments are performed to verify the theoretical analyses and to show the superiority of the proposed methods in long time simulation.