用inVAErt网络高效学习化学反应系统，解决速率参数难识别问题。
Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks
- 基于inVAErt网络构建反应系统代理模型，支持快速计算。
- 对2~20个方程的系统，误差低至10^-5~10^-3。
- 可识别不可分辨参数流形，适合复杂化学动力学研究者。
针对化学动力学中刚性常微分方程组的数据驱动建模问题，本文提出一种高效可计算的代理模型方法。首先利用条件残差网络或长短期记忆架构训练不同反应速率下的反应方程族代理模型；随后应用新兴的数据驱动框架inVAErt网络，解决从物种浓度反推反应速率、积分时间及初始条件这一欠定逆问题，该问题在文献中较少被关注。方法在包含2至20个微分方程、3至20种化学物种、3至25个反应速率参数的可逆与不可逆反应系统上验证。所提代理模型产生的相对均方根误差在低维系统中达10^-5，在大气污染模型和氢气-空气反应系统中分别为10^-4和10^-3。恢复的不可分辨反应速率流形在简单系统中可解析验证，并与高维局部可辨识性分析结果一致。
原文摘要 · Abstract (English)
We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be evaluated with high computational efficiency. We first focus on training emulators for families of reaction equations under varying reaction rates, using conditional residual networks or long-short term memory architectures. We then apply a recently proposed data-driven framework known as ``inVAErt networks'' to address the ill-posed inverse problem of inferring reaction rates, integration time, and possibly initial conditions from a target set of species concentrations - a problem that has received relatively little attention in the literature. The proposed approach is demonstrated on chemical systems with reversible and irreversible kinetics, spanning 2 to 20 differential equations, 3 to 20 chemical species, and 3 to 25 reaction rate parameters. Relative root mean squared errors produced by the proposed emulators range from $10^{-5}$ for lower-dimensional systems to $10^{-4}$ and $10^{-3}$ for an air pollution model and a hydrogen-air reaction system, respectively. Manifolds of non-identifiable reaction rates recovered by the proposed approach can be analytically verified for simple systems and are consistent with local identifiability analysis in higher dimensions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。