Research on Collective Movement Prediction Based on Graph Network

被引：0
|
作者：
Wang R. [1 ]
Cui J. [1 ]
Zhang Y. [1 ]
Zheng W. [1 ]
机构：
[1] College of Data Science, Taiyuan University of Technology, Taiyuan
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 05期
关键词：
Collective movement; Deep learning; Graph network; Self-driven;
D O I：
10.12178/1001-0548.2021107
中图分类号：
学科分类号：
摘要：
Collective dynamics is a research hotspot and frontier perspective in the field of soft matter. The synchronization mechanism of collective movement has rich potential laws and application values. This paper constructs a graph network model based on weighted collective dynamics, which learns the evolution mechanism of collective movement from the position of particles, the movement direction and the influence of neighbors, and can realize long-term prediction of the evolution movement process of the collective movement. Results show that the graph network model can predict the order parameters of the movement process, covering different noises and field of view radius, and the prediction effect is better. After the model is constructed, the value of the order parameter of the system can be obtained without complex dynamic simulation and calculation, so as to quickly quantify the synchronization degree of the collective movement, which can save time and cost, and have important significance for the intelligent control of the collective. © 2021, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：768 / 773
页数：5
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