Variational Streaming Flow: Probabilistic Forecasting in Physical Time

Excerpt

arXiv:2610.00976v1 Announce Type: new Abstract: Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve into multiple plausible futures. While flow matching is a flexible approach for probabilistic forecasting, it is computationally expensive. Streaming flow (SF) reformulates this approach to model temporal evolution efficiently by learning a continuous velocity field dire