Learning Enhanced Ensemble Filters

Sun, 15 Feb 2026 00:00:00 +0000

This work introduces the measure neural mapping enhanced ensemble filter (MNMEF), a learning-based data-assimilation method derived from a mean-field formulation of the filtering problem. Measure neural mappings extend neural operators to maps acting on probability measures; their finite-ensemble implementation uses a permutation-invariant set transformer.

The mean-field construction allows most learned parameters to be shared across ensemble sizes. A model can therefore be trained efficiently using a small ensemble and deployed at other ensemble sizes, while lightweight fine-tuning adapts a limited number of parameters such as inflation and localization.

Experiments on Lorenz-63, Lorenz-96, and Kuramoto–Sivashinsky systems show improved filtering accuracy relative to optimized classical ensemble methods across both small and larger ensembles.

Computational Physics | Bohan Chen's Personal Webpage

Learning Enhanced Ensemble Filters