Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3417-2022
https://doi.org/10.5194/gmd-15-3417-2022
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Model comparison and naming conventions', Oscar Jacquot, 14 Jan 2022
    • CC2: 'Reply on CC1', Oscar Jacquot, 19 Jan 2022
  • RC1: 'Comment on gmd-2021-402', Anonymous Referee #1, 18 Jan 2022
  • RC2: 'Comment on gmd-2021-402', Anonymous Referee #2, 11 Feb 2022
  • AC1: 'Author Response gmd-2021-402', Patrick Obin Sturm, 17 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Patrick Obin Sturm on behalf of the Authors (17 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (18 Mar 2022) by Christoph Knote
RR by Anonymous Referee #2 (25 Mar 2022)
RR by Anonymous Referee #1 (28 Mar 2022)
ED: Publish subject to technical corrections (29 Mar 2022) by Christoph Knote
AR by Patrick Obin Sturm on behalf of the Authors (31 Mar 2022)  Author's response    Manuscript
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.