论文标题
使用基于复发的神经网络操作员，用大型时间段解决牛顿运动方程
Solving Newton's Equations of Motion with Large Timesteps using Recurrent Neural Networks based Operators
论文作者
论文摘要
经典的分子动力学模拟基于求解牛顿运动方程。使用一个小的时间步，数值积分器（例如Verlet）会生成粒子轨迹作为牛顿方程的解决方案。我们介绍了使用复发性神经网络得出的操作员，这些神经网络使用过去的轨迹数据序列准确地求解牛顿方程，并使用与Verlet TimeStep相比，使用时间段高达4000倍的粒子的能量支持动力学。在许多示例问题中，包括3D系统最多16个颗粒。
Classical molecular dynamics simulations are based on solving Newton's equations of motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of particles as solutions to Newton's equations. We introduce operators derived using recurrent neural networks that accurately solve Newton's equations utilizing sequences of past trajectory data, and produce energy-conserving dynamics of particles using timesteps up to 4000 times larger compared to the Verlet timestep. We demonstrate significant speedup in many example problems including 3D systems of up to 16 particles.
