14 citations · 34 across the 16 of their papers we have counts for
4 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…
PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
Emma Kasteleyn, Timo Maier, Axel Lauer +3
Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-b…
Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher…
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Birgit Kühbacher, Fernando Iglesias-Suarez, Niki Kilbertus +1
Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of about 40-100 km remains too coarse to re…