论文标题
非圆形刚体的仿真：基于机器学习的重叠计算技术与系统尺寸独立计算成本
Simulation of Noncircular Rigid Bodies: Machine Learning Based Overlap Calculation Technique with System Size Independent Computational Cost
论文作者
论文摘要
标准分子动力学（MD）和蒙特卡洛（MC）模拟涉及球形颗粒。将这些标准仿真方法扩展到非球形案例是非平凡的。为了解决这个问题，非球体被认为是组成型球形对象的集合。随着这些组成对象的数量变大，模拟系统的计算负担也会增加。在本文中，我们提出了一种替代方法，以模拟具有成对排斥相互作用的两个维度的非圆形刚体。我们的方法基于基于机器学习的模型，该模型可以预测两个非圆形体之间的重叠。机器学习模型很容易训练，其实施的计算成本仍然独立于用于代表非圆形刚体体的成分磁盘数量。在MC模拟中使用时，与标准实现相比，我们的方法提供了很大的速度，在标准实现中，通过计算其组成磁盘的距离来完成两个刚体之间的重叠确定。我们提出的基于ML的MC方法提供了系统的非常相似的结构特征（与标准实现相比）。我们认为，这项工作是迈向对非球体刚体的时间效率模拟的第一步。
Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with spherical particles. Extending these standard simulation methodologies to the non-spherical cases is non-trivial. To circumvent this problem, non-spherical bodies are considered as a collection of constituent spherical objects. As the number of these constituent objects becomes large, the computational burden to simulate the system also increases. In this article, we propose an alternative way to simulate non-circular rigid bodies in two dimensions having pairwise repulsive interactions. Our approach is based on a machine learning (ML) based model which predicts the overlap between two non-circular bodies. The machine learning model is easy to train and the computation cost of its implementation remains independent of the number of constituents disks used to represent a non-circular rigid body. When used in MC simulation, our approach provides significant speed up in comparison to the standard implementation where overlap determination between two rigid bodies is done by calculating the distance of their constituent disks. Our proposed ML based MC method provided very similar structural features (in comparison to standard implementation) of the systems. We believe this work is a very first step towards a time-efficient simulation of non-spherical rigid bodies.
