titolo: Dynamical systems with noisy data
abstract: Reconstructing the state of a dynamical system from noisy observations is particularly challenging when the initial conditions are unknown. This talk focuses on synchronization-based and feedback control methods for data assimilation, which dynamically align model trajectories with observed data. Techniques such as nudging, observer-based schemes, and coupling strategies will be discussed, highlighting how they correct for uncertainty and stabilize the system in real time. Applications include chaotic systems and models governed by stochastic dynamics.