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Florian Sestak

2 accepted papers

2026

WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling

ICML 2026poster

Deep learning has revolutionized weather and climate modeling, yet the current landscape remains fragmented: highly specialized models are typically trained individually for distinct tasks. To unify this landscape, we introduce WIND, a single pre-trained foundation model capable of replacing special…

Cited by 0SourceScholar
2025

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities

NeurIPS 2025poster

Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space modeling paradigms have transformed image and video generation, similar approaches are more difficult for most dynamical…

Cited by 0SourcecodeScholar