Collective Behaviors and Deep Learning Applications

Understanding and modeling collective pedestrian behavior, particularly under extreme conditions, is a challenging problem that combines cognition, physics, and data analysis. In the second talk of DEEP-IN series, I will explore how deep learning can reveal the underlying principles of crowd dynamics from data. Starting with a bounded rationality framework, we demonstrate how deep learning can quantify evacuation dynamics and reveal hidden patterns in collective motion. Specifically, we demonstrate how macroscopic observables, such as entropy and kinetic energy, can be extracted from microscopic trajectories in simulations and real-world data.

This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.