Kuramoto Model

The Kuramoto model describes the synchronization of coupled oscillators, a phenomenon observed across diverse systems from biological networks to physical systems. Current research focuses on extending the model to higher-dimensional spaces (spheres, manifolds, Lie groups) and applying it to machine learning tasks, particularly in non-Euclidean geometries, and neuromorphic computing. These advancements leverage the model's ability to capture complex synchronization dynamics for applications ranging from improved graph neural networks to efficient algorithms for rotation averaging and uncertainty quantification in complex systems. The model's versatility and adaptability make it a valuable tool for understanding and modeling collective behavior in various scientific domains.