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November 20, 2025Communications PhysicsOpen Access

Hierarchical equivariant graph neural networks for forecasting collective motion in vortex clusters and microswimmers

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Authors

ALAlec J. LinotHHHaotian HangEKEva Kanso

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Overview

Hierarchical graph neural networks predict collective motion dynamics in multi-agent systems, suggesting enhanced modeling accuracy.

Key Points

  • Accurate predictions of collective motion obtained through advanced graph neural networks with hierarchical structures, ensuring better performance.
  • The study demonstrates that a new model captures both local and global behaviors in multi-agent systems effectively, outperforming conventional approaches.
  • Methodology employs improved graph neural networks tailored for complex interactions present in systems like bird flocks and fish schools.
  • Results highlight the efficiency of the proposed model, emphasizing its capability in preserving Hamiltonian dynamics in point vortices.

Cite This Study

Linot et al. (2025) studied this question.

synapsesocial.com/papers/6924f084c0ce034ddc3502d8https://doi.org/10.1038/s42005-025-02417-2
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