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

ArXi:2603.25370v1 Announce Type: cross Probabilistic forecasting provides a principled framework for uncertainty quantification in dynamical systems by representing predictions as probability distributions rather than deterministic trajectories. However, existing forecasting approaches, whether physics-based or neural-network-based, remain fundamentally trajectory-oriented: predictive distributions are usually accessed through ensembles or sampling, rather than evolved directly as dynamical objects.