DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observations to estimate latent system states. Existing filters, however, condition on a fixed history and assimilate only the most recent observation, leaving them unable to revise past states when new observations arrive. Estimates then stay tethered to a history that later observations may contradict, and errors accumulate over the assimilation run. To this end, we introduce **DAW...
| Authors | Erik Wikingsson, Martin Andrae, Tomas Landelius et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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