Speaker
Francesco Fossella
Description
Nonlinearities amplify small uncertainties in initial conditions, resulting in strong unpredictability of the dynamics. We investigate optimal strategies to integrate sparse and noisy data into forecasting models to extend the predictability horizon, with applications ranging from intermittent shell models for turbulence to the spatiotemporal complexity of Rayleigh–Bénard convection, while also considering the role of Machine Learning.
