Inferring Particle Interaction Physical Models and Their Dynamical Properties

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 trajectories 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 metrics. 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 systems applications.