This is an elementary page that collects work related to our group on hydrologic differentiable modeling (DM)

For a general overview, please see our Nature Reviews Earth and Environment perspective article: Differentiable modelling to unify machine learning and physical models for geosciences.

DM Publications

1. Feng, Dapeng*, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu*, Ming Pan, Kathryn Lawson*, and Chaopeng Shen. (2024). Deep dive into global hydrologic simulations: Harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL),  Geoscientific Model Development. doi: 10.5194/gmd-17-7181-2024
2. Song, Yalan*, Wouter Knoben, Martyn P. Clark, Dapeng Feng*, Kathryn Lawson, and Chaopeng Shen. (2024). When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling, Hydrology and Earth System Sciences. doi: hess-28-3051-2024
3. Bindas, Tadd*, Wen-Ping Tsai*, Jiangtao Liu*, Farshid Rahmani*, Dapeng Feng*, Yuchen Bian, and Chaopeng Shen. (2024). Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning, Water Resources Research. doi: 10.1029/2023WR035337 (Data Release Link)
4. Shen, Chaopeng, 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, and Kathryn Lawson*. (2023). Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth and Environment. doi: 10.1038/s43017-023-00450-9. Open-Access Online PDF 
5. Rahmani, Farshid*, Alison Appling, Kathryn Lawson*, and Chaopeng Shen. (2023). Identifying structural priors in a hybrid differentiable model for stream water temperature modeling, Water Resources Research. doi: 10.1029/2023WR034420. (Data Release Link)
6. Dwivedi, Dipankar, Xingyuan Chen, Chaopeng Shen, and Harihar Rajaram. (2023). Advancing AI and machine learning beyond predictive capabilities, Eos (Editor's Vox -- non-peer reviewed). doi: 10.1029/2023EO235032
7. Aboelyazeed, Doaa*, Chonggang Xu, Forrest M. Hoffman, Alex W. Jones, Chris Rackauckas, and Chaopeng Shen. (2023). A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations, Biogeosciences. doi: 10.5194/bg-20-2671-2023
8. Feng, Dapeng*, Hylke Beck, Kathryn Lawson*, and Chaopeng Shen. (2023). The suitability of differentiable, learnable hydrologic models, Hydrology and Earth System Sciences. doi: 10.5194/hess-27-2357-2023
9. Feng, Dapeng*, Jiangtao Liu*, Kathryn Lawson*, and Chaopeng Shen. (2022). Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy, Water Resources Research. doi:10.1029/2022WR032404
10. Tsai, Wen-Ping*, Dapeng Feng*, Ming Pan, Hylke Beck, Yuan Yang, Kathryn Lawson*, Jiangtao Liu*, and Chaopeng Shen. (2021). From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. Nature Communications,. doi: 10.1038/s41467-021-26107-z.