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Prairie Research Institute

United States

1 paper here466 citations across 1
fields
  • cs.LG1
ROR 0540f7s80OpenAlex

affiliations via OpenAlex

most citedDifferentiable modeling to unify machine learning and physical models and advance Geosciences

466 citations

researchers with a paper here
  • Chonggang Xu2 profiles2 · h 43
  • A. Appling1 · h 26
  • A. Tartakovsky1 · h 43
  • Chaopeng Shen1 · h 43
  • C. Harman1 · h 43
  • Chris Rackauckas1 · h 19
  • Daniel Kifer1 · h 49
  • D. Dwivedi1 · h 26
  • D. Feng1 · h 19
  • Doaa Aboelyazeed1 · h 4
  • F. Fenicia1 · h 39
  • F. Rahmani1 · h 17
collaborating institutions
  • Columbia UniversityUS1 paper
  • Global Institute for Water SecurityCA1 paper
  • Johns Hopkins UniversityUS1 paper
  • King Abdullah University of Science and TechnologySA1 paper
  • Lawrence Berkeley National LaboratoryUS1 paper
  • Los Alamos National LaboratoryUS1 paper
  • Massachusetts Institute of TechnologyUS1 paper
  • Pennsylvania State UniversityUS1 paper
  • Southern University of Science and TechnologyCN1 paper
  • Stanford UniversityUS1 paper
  • Swiss Federal Institute of Aquatic Science and TechnologyCH1 paper
  • Texas A&M UniversityUS1 paper

1 paper

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…

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