摘要
本文运用机器学习方法设计一维线性原子链的人工边界条件。该方法基于前馈神经网络,通过对一小部分数值解进行训练后得到人工边界条件。应用该法不需要较多先验知识,编程简单,实现速度快,算例表明数值反射较小。
In this paper,we adopt machine learning techniques to design artificial boundary conditions for one-dimensional linear atomic chain.Training a feedforward neural network with a small amount of numerical solutions,we obtain artificial boundary conditions.This approach requires little prior information,and programming and computation are fast.Numerical examples illustrate a relatively small reflection.
作者
张慊
乔丹
唐少强
ZHANG Qian;QIAO Dan;TANG Shaoqiang(Department of Mechanics and Engineering Science,College of Engineering,Peking University,Beijing 100871,China;Department of Probability and Statistics,School of Mathematical Sciences,Peking University,Beijing 100871,China)
出处
《力学与实践》
北大核心
2020年第1期13-16,共4页
Mechanics in Engineering
基金
国家自然科学基金资助项目(11832001)。
关键词
分子动力学模拟
人工边界条件
机器学习
前馈神经网络
molecular dynamics simulation
artificial boundary conditions
machine learning
feedforward neural network
