mlsanityData to 14 Oct 2026

Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation

arXiv 2210.04001 ↗First posted 8 Oct 2022cs.LG nlin.CDcited by 0

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Abstract (arXiv)

How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises coarse-grained high-resolution data. But only keeping this coarse-grained data means the rest of the high-resolution data is thrown out. We use a transfer learning approach, which can be applied to a range of machine learning models, to leverage all the high-resolution data. We use three chaotic systems to show it stabilises training, gives improved generalisation performance and results in better forecasting skill. Our code is at https://github.com/raghul-parthipan/dont_waste_data

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 8 Oct 2022.

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7 more
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