Google DeepMind's GenCast is based on diffusion: https://deepmind.google/discover/blog/gencast-predicts-weather-and-the-risks-of-extreme-conditions-with-sota-ac

Google DeepMind's GenCast is based on diffusion: https://deepmind.google/discover/blog/gencast-predicts-weather-and-the-risks-of-extreme-conditions-with-sota-accuracy/ https://deepmind.google/discover/blog/gencast-predicts-weath... (Partially) Google Research's/DeepMind's NeuralGCM is based on hybrid models using ODEs and learnt physics: https://www.nature.com/articles/s41586-024-07744-y https://www.nature.com/articles/s41586-024-07744-y Microsoft Research's Aurora on vision transformers: https://www.microsoft.com/en-us/research/blog/introducing-aurora-the-first-large-scale-foundation-model-of-the-atmosphere/ https://www.microsoft.com/en-us/research/blog/introducing-au... Huawei's Pangu Weather is also a 3D transformer I believe https://www.nature.com/articles/s41586-024-07744-y https://www.nature.com/articles/s41586-024-07744-y I just wanted to highlight that there are multiple approaches in use for the same problem / in the same domain, and GNN does not seem to be the most widely used one.