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.
@article{arxiv.2509.18811,
title = {Training-Free Data Assimilation with GenCast},
author = {Thomas Savary and François Rozet and Gilles Louppe},
journal= {arXiv preprint arXiv:2509.18811},
year = {2025}
}