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.
arXiv:2609.28015v1 Announce Type: cross Abstract: Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis upd...
arXiv:2605. 29072v2 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management.
Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not provide the residual structures or likelihood guidance required by existing ensemble filters.
arXiv:2609.35944v1 Announce Type: new Abstract: Data Assimilation (DA) aims to recover the full state of a dynamical system that is only partially observed. A solution is to use Score-based models to...
arXiv:2605. 29072v3 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management.
arXiv:2602. 23461v2 Announce Type: replace-cross Abstract: Data assimilation (DA) for compressible flows with shocks is challenging because many classical DA methods generate spurious oscillations and nonphysical features near uncertain shocks.