{"ID":2852623,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17268","arxiv_id":"2510.17268","title":"Uncertainty-aware data assimilation through variational inference","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 https://github.com/anthony-frion/Stochastic_CODA.","short_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 exte...","url_abs":"https://arxiv.org/abs/2510.17268","url_pdf":"https://arxiv.org/pdf/2510.17268v2","authors":"[\"Anthony Frion\",\"David S Greenberg\"]","published":"2025-10-20T07:54:35Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false,"code_links":[{"ID":608014,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852623,"paper_url":"https://arxiv.org/abs/2510.17268","paper_title":"Uncertainty-aware data assimilation through variational inference","repo_url":"https://github.com/anthony-frion/Stochastic_CODA","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}