FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation
Data assimilation aims to infer the state of complex dynamical systems based on observational data. However, accurate inference of the multimodal posteriors induced by nonlinear or non-injective observation operators remains a key challenge under high-dimensional and sparse observation conditions. Ensemble filters scale to high dimensions but are confined by restrictive distributional assumptions, while training-free generative filters (e.g., EnSF, EnFF) alleviate this limitation but may introduce structural errors and hinder information propagation under sparse observations. To address these issues, we propose a training-free, asymptotically exact posterior transport method. Firstly, a c...
| Authors | Shiwei Ni, Yangwen Zhang, Hang Qi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 19 September 2026 |
| Last updated at source | 26 September 2026 |
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