Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 7
Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 7
Development and technical paper
 | 
14 Apr 2023
Development and technical paper |  | 14 Apr 2023

Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks

Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger

Viewed

Total article views: 3,467 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,715 674 78 3,467 123 113 152
  • HTML: 2,715
  • PDF: 674
  • XML: 78
  • Total: 3,467
  • Supplement: 123
  • BibTeX: 113
  • EndNote: 152
Views and downloads (calculated since 20 Oct 2022)
Cumulative views and downloads (calculated since 20 Oct 2022)

Viewed (geographical distribution)

Total article views: 3,467 (including HTML, PDF, and XML) Thereof 3,381 with geography defined and 86 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Read more
Share