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Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices w
Externí odkaz:
http://arxiv.org/abs/2409.11129
Autor:
Saint-Martin, David, Geoffroy, Olivier
This is the first part of a series of two articles describing the ARP-GEM global atmosphere model version 1 and its evaluation in simulations from 55 km to 6 km resolutions. This article provides a complete description of ARP-GEM1, focusing on its ne
Externí odkaz:
http://arxiv.org/abs/2409.19083