Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
The ensemble Kalman filter (EnKF) is widely used for data assimilation in high-dimensional systems, but its performance often deteriorates for strongly nonlinear dynamics due to the structural mismatch between the Kalman update and the underlying system behavior. In this work, we propose a latent autoencoder ensemble Kalman filter (LAE-EnKF) that addresses this limitation by reformulating the assimilation problem in a learned latent space with linear and stable dynamics. The proposed method learns a nonlinear encoder--decoder together with a stable linear latent evolution operator and a consistent latent observation mapping, yielding a closed linear state-space model in the latent coordin...
| Authors | Xin T. Tong, Yanyan Wang, Liang Yan |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 29 April 2026 |
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