RESEARCH & SOURCES
Differentiable modelling to unify machine learning and physical models for geosciences
Paper authors：Chaopeng Shen · Alison P. Appling · Pierre Gentine · Toshiyuki Bandai · Hoshin Gupta · Alexandre Tartakovsky · Marco Baity-Jesi · Fabrizio Fenicia · Daniel Kifer · Li Li · Xiaofeng Liu · Wei Ren · Yi Zheng · Ciaran J. Harman · Martyn Clark · Matthew Farthing · Dapeng Feng · Praveen Kumar · Doaa Aboelyazeed · Farshid Rahmani · Yalan Song · Hylke E. Beck · Tadd Bindas · Dipankar Dwivedi · Kuai Fang · Marvin Höge · Chris Rackauckas · Binayak Mohanty · Tirthankar Roy · Chonggang Xu · Kathryn Lawson
DOI: 10.1038/s43017-023-00450-9
AI reading guide
AI summaryReviews how differentiable computation can jointly train physical components and neural networks to support process understanding, parameter estimation and geoscientific prediction.
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Read the original source ↗CAS 2025 major-category tier 1 and Top. This journal-level label does not assess this individual paper.