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FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

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

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