Predator Prey

Predator-prey dynamics, fundamental to ecological understanding, are being investigated through diverse computational approaches, aiming to model and predict complex interactions. Current research focuses on developing advanced machine learning models, including physics-informed neural networks and reinforcement learning algorithms, to simulate and analyze predator-prey behaviors in various contexts, from simple ODE systems to complex multi-agent scenarios. These studies are improving our ability to understand emergent behaviors like swarming and camouflage, with applications ranging from conservation efforts (e.g., analyzing animal-borne video data) to robotics (e.g., developing more effective autonomous agents). The insights gained contribute to a deeper understanding of ecological processes and inform the design of more sophisticated artificial systems.