[2510.17268] Uncertainty-aware data assimilation through variational inference

Computer Science > Machine Learning arXiv:2510.17268 (cs) [Submitted on 20 Oct 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Uncertainty-aware data assimilation through variational inference Authors:Anthony Frion, David S Greenberg View a PDF of the paper titled Uncertainty-aware data assimilation through variational inference, by Anthony Frion and 1 other authors View PDF HTML (experimental) Abstract:Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing deterministic machine learning approach, we propose a variational inference-based extension in which the predicted state follows a multivariate Gaussian distribution. Using the chaotic Lorenz-96 dynamics as a testing ground, we show that our new model enables to obtain nearly perfectly calibrated predictions, and can be integrated in a wider variational data assimilation pipeline in order to achieve greater benefit from increasing lengths of data assimilation windows. Our code is available at this https URL. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2510.17268 [cs.LG]   (or arXiv:2510.17268v2 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2510.17268 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Anthony Frion [view email] [v1] Mon, 20 Oct 2025 07:54:35 UTC...