Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data | Zendy

J. Brown | Zendy; Michael B. Sohn | Zendy; Utkarsh Mital | Zendy; Dipankar Dwivedi | Zendy; Haruko Wainwright | Zendy; Carl I. Steefel | Zendy; Eoin Brodie | Zendy; William D. Collins | Zendy; Daniel Jacobson | Zendy; Michael W. Mahoney | Zendy; Tianzhen Hong | Zendy; Christoph Gehbauer | Zendy; Doug Black | Zendy; Thomas W. Kirchstetter | Zendy; Daniel Arnold | Zendy; Sean Peisert | Zendy

Open Access

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Author(s) -

J. Brown,

Michael B. Sohn,

Utkarsh Mital,

Dipankar Dwivedi,

Haruko Wainwright,

Carl I. Steefel,

Eoin Brodie,

William D. Collins,

Daniel Jacobson,

Michael W. Mahoney,

Tianzhen Hong,

Christoph Gehbauer,

Doug Black,

Thomas W. Kirchstetter,

Daniel Arnold,

Sean Peisert

Publication year - 2021

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1769736

Subject(s) - nexus (standard) , data science , computer science , energy modeling , earth system science , scale (ratio) , probabilistic logic , process (computing) , forcing (mathematics) , temporal scales , uncertainty quantification , machine learning , artificial intelligence , efficient energy use , engineering , geography , ecology , cartography , climatology , geology , electrical engineering , biology , embedded system , operating system

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