ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and climate science. In practice, filtering methods rely on frame-to-frame transition models. However, these models are fragile when observations are non-Markovian (when they form only a partial slice of a higher-dimensional latent state as in real-world weather data): they tend to accumulate errors over long horizons. At the same time, learned DA methods typically commit to a single regime, either filtering (nowcasting, real-time forecasting) or smoothing (retrospective reanalysis), which splits what should be a shared...
| Authors | Yixuan Jia, Siyi Chen, Yida Pan et al. |
|---|---|
| Primary category | eess.IV |
| Published | 14 May 2026 |
| Last updated at source | 5 October 2026 |
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