Atmospheric Chemistry Surrogate Modeling With Sparse Identification of Nonlinear Dynamics
Xiaokai Yang,
Lin Guo,
Zhonghua Zheng
et al.
Abstract:Modeling atmospheric chemistry is computationally expensive and limits the widespread use of chemical transport models. This computational cost arises from solving high‐dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long‐term simulations. This may be because previous ML‐based efforts have relied on explicit Euler time integration—which is known… Show more
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