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Machine learning workflows in climate modelling: design patterns and insights from case studies.

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第一作者： Tian,Zheng

第一单位： Department of Statistics, Columbia University in the City of New York, New York, NY, USA.;Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA.

作者： Tian,Zheng ^[1] ; Subashree,Venkatasubramanian ^[2] ; Shuolin,Li ^[3] ; Amy,Braverman ^[4] ; Xinyi,Ke ^[1] ; Zhewen,Hou ^[5] ; Peter,Jin ^[6] ; Samarth,Agrawal ^[2]

作者单位： Department of Statistics, Columbia University in the City of New York, New York, NY, USA.;Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. ^[1] Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. ^[2] Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA.;Department of Data Science Institute, Columbia University in the City of New York, New York, NY, USA. ^[3] Jet Propulsion Laboratory , Pasadena, CA, USA. ^[4] Department of Statistics, Columbia University in the City of New York, New York, NY, USA. ^[5] Department of Applied Physics and Applied Mathematics, Columbia University in the City of New York, New York, NY, USA. ^[6]

关键词 climate modelling equation discovery machine learning probabilistic programming remote sensing simulation-based inference surrogate models transfer learning workflow design

DOI 10.1098/rsta.2025.0254

PMID 42207042

发布时间 2026-05-28

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Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

2026年384卷2321期

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Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

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Machine learning workflows in climate modelling: design patterns and insights from case studies.

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Machine learning workflows in climate modelling: design patterns and insights from case studies.

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