3 papers
cs.LG2026
Recursive Flow Matching
Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without in…
cs.LG2025
Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamics
Salva Rühling Cachay, Miika Aittala, Karsten Kreis +4
Diffusion models are a powerful tool for probabilistic forecasting, yet most applications in high-dimensional complex systems predict future states individually. This approach stru…
cs.LG2024
Probabilistic Emulation of a Global Climate Model with Spherical DYffusion
Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer +2
Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the dat…