A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems

Probabilistic forecasts provide a principled representation of forecast uncertainty through predictive distributions. Directly propagating predictive distributions with conventional physical models requires solving distributional evolution equations, a task well-known to be computationally prohibiti