Learning self-driven collective dynamics with graph networks.

ABSTRACT: 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 predict

SUBMITTER: Wang R 

PROVIDER: S-EPMC8752591 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature