Rewiring climate modeling with machine learning emulators
Abstract Earth system models, or simulators, are foundational for projecting climate change impacts, but their computational expense limits the number and diversity of simulations available. Machine learning-based emulators, statistical surrogates trained on simulator outputs, can replicate componen...
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| Principais autores: | , , , |
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| 格式: | Artigo |
| 語言: | Inglês |
| 出版: |
Nature Portfolio
2026-01-01
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| 叢編: | Communications Earth & Environment |
| 在線閱讀: | https://doi.org/10.1038/s43247-026-03238-z |
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