Collective Motion

Collective motion studies the emergent coordinated behavior of interacting entities, aiming to understand the underlying principles governing such phenomena and to develop methods for controlling and predicting these dynamics. Current research focuses on developing and applying diverse models, including agent-based models, deep neural networks, and mean-field approaches, to analyze both biological systems (e.g., animal flocks, cell migration) and engineered systems (e.g., robot swarms). This field is significant for its potential to improve our understanding of complex systems and to enable the design of more efficient and robust artificial systems, with applications ranging from robotics and traffic control to epidemiology and financial modeling.