Incomplete Data, Complete Dynamics: A Diffusion Approach

ArXi:2509.20098v2 Announce Type: replace Learning physical dynamics from data is a fundamental challenge in machine learning and scientific modeling. Real-world observational data are inherently incomplete and irregularly sampled, posing significant challenges for existing data-driven approaches. In this work, we propose a principled diffusion-based framework for learning physical systems from incomplete