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arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.
AECSF: Adaptive Ensemble Conditional Score Filtering for High‑Dimensional Nonlinear Data Assimilation proposes a training‑free adaptive ensemble conditional score filter that constructs an analytically tractable score estimator using the conditional Tweedie identity. The method employs a shared adaptive weighted proposal ensemble to efficiently estimate conditional means for each noisy reverse particle, updating the proposal within the same reverse‑diffusion run. Theoretical analysis characterizes when a fixed weighted proposal yields the exact noisy posterior score and bounds the error between conditional‑mean estimation and reverse‑sampling endpoint error, while numerical experiments show improved posterior sampling and nonlinear filtering accuracy in high‑dimensional problems with limited forecast ensembles.
arXiv:2512. 18928v4 Announce Type: replace Abstract: This work introduces a novel nonlinear optimal filtering method, termed the Ensemble Schr{\"o}dinger Bridge nonlinear filter.
arXiv:2610.32148v1 Announce Type: cross Abstract: Background-error covariance models are central to atmospheric data assimilation because they spread partial observational information across space, v...
arXiv:2508. 13313v4 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations.
arXiv:2607. 12975v1 Announce Type: cross Abstract: Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations.