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This study introduces a novel machine learning approach to analyze self-driven collective motion and its phase transitions. The new method quantifies synchronization and predicts system evolution, offering insights into complex physical systems.

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Area of Science:

  • Physics
  • Complex Systems
  • Statistical Mechanics

Background:

  • The nature of self-driven collective motion and its dynamic phase transitions remain poorly understood despite extensive theoretical research.
  • Current machine learning methods rely on artificially extracted features to infer phase transition processes.

Purpose of the Study:

  • To develop a novel machine learning-based order parameter for quantifying synchronization in self-driven collective systems.
  • To construct a graph network model for predicting the long-term evolution of these systems without manual feature engineering.

Main Methods:

  • A new order parameter was developed using machine learning to measure synchronization based on cluster numbers.
  • A graph network model was employed to predict system dynamics directly from initial particle positions.

Main Results:

  • The proposed method demonstrates strong predictive power for the long-term evolution of self-driven collective systems.
  • The model is robust and performs well across various noise levels.

Conclusions:

  • This machine learning approach offers a new perspective on quantifying synchronization and predicting dynamics in self-driven collective motion.
  • The methodology provides a valuable reference for studying other physical systems characterized by local interactions.