Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4435-2020
https://doi.org/10.5194/gmd-13-4435-2020
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

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Patrick Obin Sturm on behalf of the Authors (10 Jul 2020)  Author's response   Manuscript 
ED: Publish as is (31 Jul 2020) by David Topping
AR by Patrick Obin Sturm on behalf of the Authors (31 Jul 2020)
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.