3 papers
cs.LG2025
PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models
Yongquan Qu, Matthieu Blanke, Sara Shamekh +1
Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizin…
cs.LG2025
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
Matthieu Blanke, Yongquan Qu, Sara Shamekh +1
Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibilit…
physics.ao-ph2025
Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
Hang Fan, Lei Bai, Ben Fei +6
Data assimilation (DA) integrates observations with model forecasts to produce optimized atmospheric states, whose physical consistency is critical for stable weather forecasting a…