发表机构
LUT University; Atmospheric Modelling Center-Lahti; University of Helsinki; Saarland University; pi-numerics（拉彭兰塔-拉赫蒂工业大学; 拉赫蒂大气模拟中心; 赫尔辛基大学; 萨尔大学; pi数值公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估了神经网络优化与MCMC两种方法在大气化学机理反应速率推断中的表现，发现MCMC在高噪声下更稳健，而神经网络优化在低噪声下更高效，两者互补。
AI 中文摘要
约束反应速率系数是显式大气化学机理开发中的一个核心挑战，尤其是在自氧化系统中，许多反应途径仅能通过高分辨率质谱间接观测。在本研究中，我们使用具有已知真实值的合成数据，针对一个玩具案例的自氧化机理评估了速率系数优化方法。比较了两种互补的方法：ODE约束的神经网络优化，它提供不确定速率系数的有效点估计；以及马尔可夫链蒙特卡洛（MCMC）方法，它采样速率系数的后验分布并量化参数不确定性。这些方法在直接浓度观测和不同噪声水平下的质谱观测中进行了测试。对于未扰动和低噪声的合成观测，两种方法都收敛到已知的速率系数，其中神经网络优化器提供了更快的点估计。然而，在高噪声条件下（信噪比约为S/N=1），MCMC在恢复速率系数方面明显更加稳健。后验分析表明，即使在低噪声下，质谱聚合也会拓宽可信区间，并且高噪声质谱可能使许多单独的反应速率弱可识别。然而，后验预测验证表明，由MCMC约束的宽参数不确定性与可观测质谱的准确再现保持一致。这些结果表明，点估计和贝叶斯采样方法提供了互补的信息：神经网络优化对信息丰富的数据有效，而MCMC对于诊断噪声或聚合逆问题中的不确定性、非唯一性和可识别性至关重要。
英文摘要
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reaction pathways are only indirectly observed through high-resolution mass spectrometry. In this study, we evaluate rate-coefficient optimisation methods for a toy-case autoxidation mechanism using synthetic data with known ground truth. Two complementary approaches are compared: ODE-constrained neural-network optimisation, which provides efficient point estimates of uncertain rate coefficients, and the Markov Chain Monte Carlo (MCMC) approach, which samples the posterior distribution of rate coefficients and quantifies parameter uncertainty. The methods are tested using direct concentration observations and mass-spectral observations under different noise levels. For unperturbed and low-noise synthetic observations, both methods converged towards the known rate coefficients, with the neural-network optimiser providing faster point estimates. Under high-noise conditions (with the signal-to-noise ratio approximately S / N = 1), however, MCMC was substantially more robust in recovering the rate coefficients. The posterior analysis shows that mass-spectral aggregation broadens credible intervals even at low noise, and that high-noise mass spectra can leave many individual reaction rates weakly identifiable. Posterior predictive validation nevertheless shows how broad parameter uncertainty constrained by MCMC remains consistent with accurate reproduction of the observable mass spectrum. These results demonstrate that point-estimation and Bayesian sampling methods provide complementary information: neural-network optimisation is effective for informative data, whereas MCMC is essential for diagnosing uncertainty, non-uniqueness, and identifiability in noisy or aggregated inverse problems.
