Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 8
Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3417-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3417-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 8
Development and technical paper
 | 
28 Apr 2022
Development and technical paper |  | 28 Apr 2022

Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)

Patrick Obin Sturm and Anthony S. Wexler

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Short summary
Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
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