459 citations · 966 across the 23 of their papers we have counts for
3 papers · 1 filter
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Jurij Schönfeld, Tom Beucler, Julien Savre +2
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, th…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Climate-Invariant Machine Learning
Tom Beucler, Pierre Gentine, Janni Yuval +10
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models requir…