Events

Date: 5 February 2025   Time: 12:00 - 13:00     

Location: MB-503

In complex systems, networks of interconnected components often evolve based on intrinsic rhythms. A key challenge is to understand these systems by inferring appropriate models from observational data. In this work, we explore methods for learning the underlying dynamics of such systems and show that the resulting models may reveal higher-order interactions which were not initially part of the model. We also examine how these emergent structures relate to transitions in the system, particularly towards synchronization. These findings provide a step towards better understanding and controlling the behavior of complex systems.