Learning Swarm Interaction Dynamics from Density Evolution
This work offers an alternative approach for studying animal flock coordination, controlling large networked systems, and defending against adversarial drone attacks, but it is incremental as it builds on existing Cucker-Smale models.
The authors tackled the problem of understanding coordinated swarm movements by proposing a learning scheme to estimate interaction laws from density observations, resulting in an efficient method that solves mean-field hydrodynamic equations as augmented PDEs.
We consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker-Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of PDEs. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.