AI RESEARCH
On Neural Scaling Laws for Weather Emulation through Continual Training
arXiv CS.LG
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ArXi:2603.25687v1 Announce Type: new Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building foundation models in Natural Language Processing and Computer Vision. We study neural scaling in Scientific Machine Learning, focusing on models for weather forecasting. To analyze scaling behavior in as simple a setting as possible, we adopt a minimal, scalable, general-purpose Swin Transformer architecture, and we use continual.