The Ensemble Schr{\"o}dinger Bridge filter for Nonlinear Data Assimilation

This work puts forward a novel nonlinear optimal filter namely the Ensemble Schr¨odinger Bridge nonlinear
filter. The proposed filter finds marriage of the standard prediction procedure and the diffusion generative
modeling for the analysis procedure to realize one filtering step. The designed approach finds no structural
model error, and it is derivative free, training free and highly parallizable. Experimental results show that
the designed algorithm performs well given highly nonlinear dynamics in (mildly) high dimension up to 40
or above under a chaotic environment. It also shows better performance than classical methods such as the
ensemble Kalman filter and the Particle filter in numerous tests given different level of nonlinearity. Future
work will focus on extending the proposed approach to practical meteorological applications and establishing
a rigorous convergence analysis.