Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2037-2023
https://doi.org/10.5194/gmd-16-2037-2023
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

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Latest update: 09 Dec 2024
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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.