Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
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 quant...
| Authors | Valery Ashu, Wenqing Peng, Zhi-Song Liu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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