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Accurate and efficient splitting methods for dissipative particle dynamics

by   Xiaocheng Shang, et al.

We study numerical methods for dissipative particle dynamics (DPD), which is a system of stochastic differential equations and a popular stochastic momentum-conserving thermostat for simulating complex hydrodynamic behavior at mesoscales. We show that novel splitting methods are able to substantially improve the accuracy and efficiency of DPD simulations in a wide range of the friction coefficients, particularly in the extremely large friction limit that corresponds to a fluid-like Schmidt number, a key issue in DPD. Various numerical experiments are performed to demonstrate the superiority of the newly proposed methods over popular alternative schemes in the literature.


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