{"ID":2867041,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18811","arxiv_id":"2509.18811","title":"Training-Free Data Assimilation with GenCast","abstract":"Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical systems. Our method builds on particle filters, a class of data assimilation algorithms, and does not require any further training. As a guiding example throughout this work, we illustrate our methodology on GenCast, a diffusion-based model that generates global ensemble weather forecasts.","short_abstract":"Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical syst...","url_abs":"https://arxiv.org/abs/2509.18811","url_pdf":"https://arxiv.org/pdf/2509.18811v2","authors":"[\"Thomas Savary\",\"François Rozet\",\"Gilles Louppe\"]","published":"2025-09-23T08:59:44Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.ao-ph\"]","methods":"[\"Diffusion Model\"]","has_code":false}