Lagrangian particle methods based on detailed atomic and molecular and nanoscale systems. However, the maximum time step is limited by the smallest oscillation period of the fastest atomic motion, rendering long-time simulations very expensive. To resolve this bottleneck, we propose a supervised parallel-in-time algorithm for stochastic dynamics to accelerate long-time Lagrangian particle simulations. Our method is inspired by bottom-up coarse-graining projections that yield mean-field hydrodynamic behavior in the continuum limit.
Revised: March 23, 2020 |
Published: September 15, 2019
Citation
Blumers A.L., Z. Li, and G.E. Karniadakis. 2019.Supervised paralled-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to hydrodynamics.Journal of Computational Physics 393.PNNL-SA-151156.doi:10.1016/j.jcp.2019.05.016