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

2026-09-19

AI Summary

为解决高维稀疏观测条件下非线性或非单射观测算子引起的多模态后验准确推断问题，提出了一种无需训练、渐近精确的协方差感知后验传输方法。

Abstract

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 covariance-aware posterior transport scheme is designed, which embeds the forecast cross-covariance into flow-based transport and accurately recovers unobserved states while preserving the non-Gaussian posterior structure. Furthermore, the method combines a tractable observation-adaptive proposal with posterior correction, ensuring accurate approximation of the nonlinear posterior distribution. Finally, we establish the corresponding posterior flow theory, from which the asymptotic exactness of the proposed method relative to finite-ensemble surrogates and the Wasserstein error bound are derived. Experiments on Double-Well, Lorenz-96, and Kolmogorov flow show that the proposed method captures complex posterior structure and remains accurate under sparse, nonlinear, and non-injective observations. In the sparse non-injective setting, it reduces RMSE by 56% relative to the best baseline.

Problem

• data assimilation
• high-dimensional
• sparse observations
• nonlinear observation operators
• multimodal posteriors

Innovation

• Covariance-Aware Posterior Transport
• Flow-Based Transport
• Observation-Adaptive Proposal
• Posterior Correction
• Asymptotic Exactness