Ensemble Kalman Filtering

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Improving Ensemble Filters with Flow Matching
Sep 23, 2026Haoyuan Chen, Alexandre ThiéryData Assimilation Ensemble Kalman Filtering
Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546
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National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Yaowu (Shenzhen) Technology Co., Ltd., Shenzhen 518052, China
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School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China · School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, China
Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Kalman Filtering Data Assimilation
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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Jun 9, 2026Batu Candan, Mohammed Atallah, Simone Servadio +1Kalman Filtering Embodied Navigation
Batu Candan, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Mohammed Atallah, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Dr. Simone Servadio, Assistant Professor, Iowa State University, Ames, IA 50011, USA +1
Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
Jun 1, 2026Jiho Lee, Nisar R. Ahmed, Rebecca RussellKalman Filtering Self-Supervised Learning
Charles Stark Draper Laboratory, Inc., Cambridge, MA · Ann and H. J. Smead Department of Aerospace Engineering Sciences at the University of Colorado, Boulder, Boulder, CO · Draper
Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
May 18, 2026Kenan Majewski, Marcin ŻugajKalman Filtering UAV Navigation
Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, Nowowiejska 24, Warsaw, 00-665, Poland
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
May 4, 2026Tianlu Lu, Asif Sijan, Thomas Noh +2Kalman Filtering Object Pose Estimation
Information and Computer Sciences Department at the University of Hawaii at Manoa · John A. Burns School of Medicine at the University of Hawaii at Manoa
Flow Matching for Efficient and Scalable Data Assimilation
Aug 18, 2025Taos Transue, Bohan Chen, So Takao +1Flow Matching Kalman Filtering
Department of Mathematics and Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, 84112 · The Computing and Mathematical Sciences Department, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125

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Improving Ensemble Filters with Flow Matching

Haoyuan Chen, Alexandre Thiéry

Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546

Data Assimilation Ensemble Kalman Filtering

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Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF uses a localized Gaussian source during training, transports forecast ensemble members from a baseline filter at deployment, and conditions its velocity field on ensembles from that baseline filter and the observation. The proposed model therefore learns a nonlinear update while mapping each baseline ensemble independently. For sparsely observed dynamical systems, FlowEF improves both deterministic and probabilistic metrics over all four classical ensemble filters. It also achieves the best performance among the state-of-the-art generative data assimilation models.

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