408 citations · 414 across the 5 of their papers we have counts for
5 papers
Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency
Maren Höver, Milan Klöwer, Christian Schroeder de Witt +1
Machine learning-based weather prediction is revolutionizing weather forecasting by learning from weather data in present-day climate. However, generalization to other climates rem…
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
Ning Liu, Yiming Fan, Xianyi Zeng +3
Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic is to learn the govern…
Neural General Circulation Models for Weather and Climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore +13
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics…
Earth Virtualization Engines -- A Technical Perspective
Torsten Hoefler, Bjorn Stevens, Andreas F. Prein +17
Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide intera…
Climate Modelling in Low-Precision: Effects of both Deterministic & Stochastic Rounding
E. Adam Paxton, Matthew Chantry, Milan Klöwer +2
Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure roundi…