Flow Matching for Efficient and Scalable Data Assimilation
The ensemble flow filter (EnFF) is a training-free data-assimilation framework that uses flow matching to transform a forecast ensemble into samples from the filtering distribution. Its Monte Carlo flow-field estimator and localized observation guidance avoid model training while retaining the flexibility of generative flow design.
Jun 15, 2026
Flow Matching for Data Assimilation Accepted in SIAM/ASA JUQ
Our ensemble flow filter brings training-free flow matching to efficient, scalable data assimilation.
Jun 15, 2026