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Published April 5, 2022 | Supplemental Material
Journal Article Open

A Neural Network-Assisted Euler Integrator for Stiff Kinetics in Atmospheric Chemistry


Atmospheric chemistry, characterized by highly coupled sets of ordinary differential equations (ODEs), is dynamically stiff owing to the fact that both fast and slow processes exist simultaneously. We develop here a neural network-assisted Euler integrator for the kinetics of atmospheric chemical reactions. We show that the integral kernel of the chemical reaction system can be represented by a neural network. The stiff kinetics of the atmospheric H₂O₂/OH/HO₂ system, involving 3 species and 4 reactions, and a simplified air pollution mechanism, involving 20 species and 25 reactions, are developed here in detail as illustrations of the neural network Euler integrator. The algorithm developed accelerates the numerical integration of large sets of coupled stiff ODEs by at least one order of magnitude by avoiding the intensive linear algebra that is required in traditional stiff ODE solvers; moreover, the mechanism-specific neural network-assisted algorithm can be readily coupled to other modules in a three-dimensional atmospheric chemical transport model.

Additional Information

© 2022 American Chemical Society. Received: November 10, 2021; Revised: March 7, 2022; Accepted: March 8, 2022; Published: March 18, 2022. We acknowledge support by the Camille and Henry Dreyfus Foundation. The authors declare no competing financial interest.

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August 22, 2023
October 23, 2023