466 citations · 786 across the 2 of their papers we have counts for
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cs.LG2023★ 466 cited
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
cs.LG2020★ 320 cited
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Wen-Ping Tsai, Dapeng Feng, Ming Pan +5
The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibr…