Physically-constrained ML projections: Difference between revisions
update
(create page) |
(update) |
||
Line 3:
{{Disclaimer}}
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
==Background Readings==
|