Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4435-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4435-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
Development and technical paper
 | 
22 Sep 2020
Development and technical paper |  | 22 Sep 2020

A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example

Patrick Obin Sturm and Anthony S. Wexler

Viewed

Total article views: 2,528 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,824 641 63 2,528 77 70
  • HTML: 1,824
  • PDF: 641
  • XML: 63
  • Total: 2,528
  • BibTeX: 77
  • EndNote: 70
Views and downloads (calculated since 28 Apr 2020)
Cumulative views and downloads (calculated since 28 Apr 2020)

Viewed (geographical distribution)

Total article views: 2,528 (including HTML, PDF, and XML) Thereof 2,344 with geography defined and 184 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Read more