[论文] KPPax: preprocess differentiable kinetic ODE for machine learning in a...

论文概要

研究领域: AI4Science / 大气化学 作者: Wenqing Peng, Michael Boy, Zhi-Song Liu 发布时间: 2026-10-27 arXiv: 2610.37655

中文摘要

大气化学机制通常以Kinetic PreProcessor（KPP）语言分发，KPP生成高效的Fortran或C代码，但无法融入机器学习模型所在的可微分编程框架。本文提出KPPax——一个动力学预处理器，能从KPP语言描述的机制生成可微分、即时编译、可向量化的JAX化学代码。KPPax在log空间用稀疏操作评估速率定律以保持可微分性，并使用diffrax库的隐式求解器及前向/反向模式伴随积分刚性系统，无需手工推导伴随代码即可获得精确灵敏度。团队用POLLU基准机制演示了两个应用：其一，自动微分Jacobian计算出的Fisher信息矩阵在任何拟合前就能预测流动管类实验设计能识别25个速率系数中的哪些；其二，神经网络排放清单修正模型先在逐场景4D-Var反演上离线训练，再通过化学模型在线微调，将生物源修正误差降低2.4-5倍。

原文摘要

Atmospheric chemical mechanisms are commonly distributed in the language of the Kinetic PreProcessor (KPP), which generates efficient Fortran or C code but does not fit into the differentiable programming frameworks in which machine-learning models are developed. We present KPPax, a kinetic preprocessor that generates differentiable, just-in-time compiled, and vectorisable chemistry code in JAX from mechanisms written in the KPP language. KPPax evaluates the rate law with sparse operations in log space, which keeps it differentiable, and integrates the stiff system with the implicit solvers and the forward- and reverse-mode adjoints of the \texttt{diffrax} library, so that exact sensitivities are obtained without hand-derived adjoint code. We demonstrate two applications with the POLLU benchmark mechanism. In the first, the Fisher information matrix computed from an automatically differentiated Jacobian predicts, before any fit, which of the 25 rate coefficients a flow-tube-like design can identify, and noisy synthetic estimation confirms the predicted uncertainties of the nine identifiable rate coefficients to within about 30\% while revealing clusters of fast reactions of which only ratios are constrained. In the second, a neural-network correction of an emission inventory is trained offline on per-scenario 4D-Var retrievals and then fine-tuned online through the chemical model, which reduces the error of the biogenic correction by a factor of 2.4--5 and recovers information on low-temperature emissions that the individual retrievals lose. KPPax makes KPP mechanisms directly usable for parameter estimation and hybrid physics--machine-learning modelling.

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*自动采集于 2026-10-29*

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