Inferring Particle Interaction Physical Models and Their Dynamical Properties

Authors

Ion Matei, Christos Mavridis, John S. Baras, Maksym Zhenirovskyy

Abstract

We propose a framework based on port-Hamiltonian modeling formalism aimed at learning interaction models between particles (or networked systems) and dynamical properties such as trajectory symmetries and conservation laws of the ensemble (or swarm). The learning process is based on approaches and platforms used for large scale optimization and uses features such as automatic differentiation to compute gradients of optimization loss functions. We showcase our approach on the Cucker-Smale particle interaction model, which is first represented in a port-Hamiltonian form, and for which we re-discover the interaction model, and learn dynamical properties that are previously proved analytically. Our approach has the potential for discovering novel particle cooperation rules that can be extracted and used in cooperative control system applications.

Citation

• Journal: 2019 IEEE 58th Conference on Decision and Control (CDC)
• Year: 2019
• Volume:
• Issue:
• Pages: 4615–4621
• Publisher: IEEE
• DOI: 10.1109/cdc40024.2019.9029524

BibTeX

@inproceedings{Matei_2019,
  title={{Inferring Particle Interaction Physical Models and Their Dynamical Properties}},
  DOI={10.1109/cdc40024.2019.9029524},
  booktitle={{2019 IEEE 58th Conference on Decision and Control (CDC)}},
  publisher={IEEE},
  author={Matei, Ion and Mavridis, Christos and Baras, John S. and Zhenirovskyy, Maksym},
  year={2019},
  pages={4615--4621}
}

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References

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