Learning the Whole Filtering Distribution with Proper Scoring Rules
Our new PSEF framework learns calibrated ensemble filters from simulated trajectories without requiring the true filtering distribution as a training target.
Jun 25, 2026
Learning Probabilistic Filters with Strictly Proper Scoring Rules
The proper scoring ensemble filter (PSEF) learns an ensemble analysis operator that targets the complete Bayesian filtering distribution rather than only its conditional mean. The operator takes a forecast ensemble and a new observation as input and returns an analysis ensemble. A permutation-invariant transformer ensures that the result respects the exchangeability of ensemble members and can be evaluated at different ensemble sizes.
Jun 25, 2026
Learning Enhanced Ensemble Filters
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.
Feb 15, 2026
Learning Enhanced Ensemble Filters Published in JCP
Our measure neural mapping enhanced ensemble filter has been published in the Journal of Computational Physics.
Dec 11, 2025