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 prohibitive for high-dimensional nonlinear systems. Consequently, multi-step predictive distributions are commonly approximated using ensembles or sampled trajectories. A novel distribution-to-distribution (D2D) neural probabilistic forecasting framework is proposed with compatible distributional encoding and decoding interfaces around a replaceable neural forecasting module. Kernel mean embeddings repr...
| Authors | Tianlin Yang, Hailiang Du, Louis Aslett |
|---|---|
| Primary category | stat.ML |
| Published | 26 March 2026 |
| Last updated at source | 14 October 2026 |
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