Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study

Abstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. Here, we critically analyze this promise in the context of large‐scale parameter optimization with the Noah‐MP land model as an example. The differ...

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書誌詳細

主要な著者:	Shahryar K. Ahmad, Sujay V. Kumar, Clara Draper, Rolf H. Reichle
フォーマット:	Artigo
言語:	Inglês
出版事項:	Wiley 2025-02-01
シリーズ:	Geophysical Research Letters
主題:	machine learning land surface model calibration soil moisture genetic algorithm transferability
オンライン･アクセス:	https://doi.org/10.1029/2024GL112893
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