Accurate and efficient splitting methods for dissipative particle dynamics

Abstract

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 propose a new splitting method that is 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 on both equilibrium and transport properties are performed to demonstrate the superiority of the newly proposed method over popular alternative schemes in the literature.

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Cited by in corpus (4)

• Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernel
• A stochastic Hamiltonian formulation applied to dissipative particle dynamics
• Stochastic Norton dynamics: An alternative approach for the computation of transport coefficients in dissipative particle dynamics
• NySALT: Nyström-type inference-based schemes adaptive to large time-stepping