积分器
常微分方程
欧拉法
人工神经网络
颂歌
刚性方程
欧拉公式
反向欧拉法
半隐式欧拉法
应用数学
核(代数)
数值积分
动力学
化学
欧拉方程
控制理论(社会学)
生物系统
微分方程
计算机科学
数学
数学分析
物理
经典力学
人工智能
带宽(计算)
控制(管理)
组合数学
计算机网络
生物
作者
Yuanlong Huang,John H. Seinfeld
标识
DOI:10.1021/acs.est.1c07648
摘要
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.
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