Difference between revisions of "Physically-constrained ML projections"

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Hybrid modelling<ref>{{Cite journal|last=Reichstein|first=Markus|last2=Camps-Valls|first2=Gustau|last3=Stevens|first3=Bjorn|last4=Jung|first4=Martin|last5=Denzler|first5=Joachim|last6=Carvalhais|first6=Nuno|last7=Prabhat|date=2019-02|title=Deep learning and process understanding for data-driven Earth system science|url=https://www.nature.com/articles/s41586-019-0912-1.|journal=Nature|language=en|volume=566|issue=7743|pages=195–204|doi=10.1038/s41586-019-0912-1|issn=1476-4687}}</ref>, by incorporating physical-constraints into data-driven ML or deep learning models is a promising field of leveraging the large amounts of data available from observational products, while making use of physical constraints present in the climate system, to ensure robust projections and extrapolating well outside of the training [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL085291 data]. The output from physics-driven GCM climate models can be used for a "perfect model test" of the ML models, before the ML model is applied to make projections based on the observations<ref>{{Cite journal|last=Schlund|first=Manuel|last2=Eyring|first2=Veronika|last3=Camps‐Valls|first3=Gustau|last4=Friedlingstein|first4=Pierre|last5=Gentine|first5=Pierre|last6=Reichstein|first6=Markus|date=2020|title=Constraining Uncertainty in Projected Gross Primary Production With Machine Learning|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005619|journal=Journal of Geophysical Research: Biogeosciences|language=en|volume=125|issue=11|pages=e2019JG005619|doi=10.1029/2019JG005619|issn=2169-8961}}</ref>.
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Hybrid modelling<ref>{{Cite journal|last=Reichstein|first=Markus|last2=Camps-Valls|first2=Gustau|last3=Stevens|first3=Bjorn|last4=Jung|first4=Martin|last5=Denzler|first5=Joachim|last6=Carvalhais|first6=Nuno|last7=Prabhat|date=2019-02|title=Deep learning and process understanding for data-driven Earth system science|url=https://www.nature.com/articles/s41586-019-0912-1.|journal=Nature|language=en|volume=566|issue=7743|pages=195–204|doi=10.1038/s41586-019-0912-1|issn=1476-4687}}</ref>, by incorporating physical-constraints into data-driven ML or deep learning models is a promising field of leveraging the large amounts of data available from observational products, while making use of physical constraints present in the climate system, to ensure robust projections and extrapolating well outside of the training data<ref>{{Cite journal|last=Zhao|first=Wen Li|last2=Gentine|first2=Pierre|last3=Reichstein|first3=Markus|last4=Zhang|first4=Yao|last5=Zhou|first5=Sha|last6=Wen|first6=Yeqiang|last7=Lin|first7=Changjie|last8=Li|first8=Xi|last9=Qiu|first9=Guo Yu|date=2019|title=Physics-Constrained Machine Learning of Evapotranspiration|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL085291|journal=Geophysical Research Letters|language=en|volume=46|issue=24|pages=14496–14507|doi=10.1029/2019GL085291|issn=1944-8007}}</ref>. The output from physics-driven GCM climate models can be used for a "perfect model test" of the ML models, before the ML model is applied to make projections based on the observations<ref>{{Cite journal|last=Schlund|first=Manuel|last2=Eyring|first2=Veronika|last3=Camps‐Valls|first3=Gustau|last4=Friedlingstein|first4=Pierre|last5=Gentine|first5=Pierre|last6=Reichstein|first6=Markus|date=2020|title=Constraining Uncertainty in Projected Gross Primary Production With Machine Learning|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JG005619|journal=Journal of Geophysical Research: Biogeosciences|language=en|volume=125|issue=11|pages=e2019JG005619|doi=10.1029/2019JG005619|issn=2169-8961}}</ref>.
 
==Background Readings==
 
==Background Readings==
   

Latest revision as of 18:03, 25 January 2021

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Hybrid modelling[1], by incorporating physical-constraints into data-driven ML or deep learning models is a promising field of leveraging the large amounts of data available from observational products, while making use of physical constraints present in the climate system, to ensure robust projections and extrapolating well outside of the training data[2]. The output from physics-driven GCM climate models can be used for a "perfect model test" of the ML models, before the ML model is applied to make projections based on the observations[3].

Background Readings[edit | edit source]

Conferences, Journals, and Professional Organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Future Directions[edit | edit source]

Relevant Groups and Organizations[edit | edit source]

References[edit | edit source]

  1. Reichstein, Markus; Camps-Valls, Gustau; Stevens, Bjorn; Jung, Martin; Denzler, Joachim; Carvalhais, Nuno; Prabhat (2019-02). "Deep learning and process understanding for data-driven Earth system science". Nature. 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1. ISSN 1476-4687. Check date values in: |date= (help)
  2. Zhao, Wen Li; Gentine, Pierre; Reichstein, Markus; Zhang, Yao; Zhou, Sha; Wen, Yeqiang; Lin, Changjie; Li, Xi; Qiu, Guo Yu (2019). "Physics-Constrained Machine Learning of Evapotranspiration". Geophysical Research Letters. 46 (24): 14496–14507. doi:10.1029/2019GL085291. ISSN 1944-8007.
  3. Schlund, Manuel; Eyring, Veronika; Camps‐Valls, Gustau; Friedlingstein, Pierre; Gentine, Pierre; Reichstein, Markus (2020). "Constraining Uncertainty in Projected Gross Primary Production With Machine Learning". Journal of Geophysical Research: Biogeosciences. 125 (11): e2019JG005619. doi:10.1029/2019JG005619. ISSN 2169-8961.