Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan, 030060, China.
Center for Healthy Big Data, Changzhi Medical College, Changzhi, 046000, China.
Sci Rep. 2022 Jan 11;12(1):500. doi: 10.1038/s41598-021-04456-5.
Despite decades of theoretical research, the nature of the self-driven collective motion remains indigestible and controversial, while the phase transition process of its dynamic is a major research issue. Recent methods propose to infer the phase transition process from various artificially extracted features using machine learning. In this thesis, we propose a new order parameter by using machine learning to quantify the synchronization degree of the self-driven collective system from the perspective of the number of clusters. Furthermore, we construct a powerful model based on the graph network to determine the long-term evolution of the self-driven collective system from the initial position of the particles, without any manual features. Results show that this method has strong predictive power, and is suitable for various noises. Our method can provide reference for the research of other physical systems with local interactions.
尽管经过了几十年的理论研究，自驱动集体运动的本质仍然难以理解和存在争议，而其动态的相变过程则是一个主要的研究问题。最近的方法提出使用机器学习从各种人为提取的特征中推断相变过程。在本论文中，我们提出了一种新的序参量，通过使用机器学习从聚类数量的角度来量化自驱动集体系统的同步程度。此外，我们构建了一个基于图网络的强大模型，从粒子的初始位置确定自驱动集体系统的长期演化，而无需任何人工特征。结果表明，该方法具有很强的预测能力，适用于各种噪声。我们的方法可以为具有局部相互作用的其他物理系统的研究提供参考。
