most citedA Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model

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physics.ao-ph2026

Hybrid physics-data-driven modeling for sea ice thermodynamics and transfer learning

Giovanni De Cillis, Alberto Carrassi, Julien Brajard +5

This study explores a physics-data driven hybrid approach for sea-ice column physics models, in which a machine learning (ML) component acts as a state-dependent parameterization o…

physics.ao-ph2025★ 1 cited

Generative AI models capture realistic sea-ice evolution from days to decades

Tobias Sebastian Finn, Marc Bocquet, Pierre Rampal +4

Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invarian…