(untitled) (Communications in Computational Physics, 2019)
Numerical integration of stiff stochastic differential equations based on stochastic computational singular perturbation (SCSP) was recently developed in [62]. In this paper, a modified stochastic computational singular perturbation (MSCSP) method is considered. Similar to what was proposed in [26] for deterministic chemical reaction systems, the current study applies the sensitivity derivatives of the forcing terms with respect to the state variables to measure the reaction scales, which leads to a quasi-steady state equation for the fast species. This yields explicit large-step integrators for stochastic fast-slow stiff differential equations systems, which removes the expensive eigen-calculations of the standard SCSP integrators. The efficiency of the MSCSP integrators is demonstrated with the benchmark stochastic Davis-Skodje model and a nonlinear catalysis model under certain stochastic disturbances.