Multi-Agent-Based Dynamic Model of Fish Collective Behavior

In the natural world, numerous fish species exhibit collective behavior to gain advantages, such as improved foraging, predator avoidance, and swimming efficiency. However, the mechanisms underlying the formation of these collective behaviors remain unclear. To investigate this phenomenon and elucidate its underlying principles, we have developed a dynamic model incorporating empirical social rules and fluid hydrodynamic effects, including the intricate wake patterns that trail swimming fish. By leveraging this flow-physics-informed approach, we can simulate the collective swimming behavior of fish in diverse scenarios with remarkable realism and comprehensiveness.

Cite: A Flow-Physics-Informed Dynamic Model of Collective Swimming in Fish

Animations of fish schooling using the dynamic multi-agent-based model.