用三阶段方法解决化学反应速率估计中的刚性难题，提升模型稳定性和精度。
SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation
- 分三阶段训练：先拟合浓度轨迹，再预训练反应网络，最后微调速率系数。
- 在合成与真实大气数据集上均实现高精度速率系数估计，收敛更稳定。
- 适合从事化学动力学建模与物理信息神经网络研究的学者使用。
从复杂化学反应中估计速率系数对推动详细化学发展至关重要。然而，真实大气化学系统固有的刚性导致训练不稳定和收敛困难，制约了基于学习的方法在速率系数估计中的应用。为此，我们提出一种针对刚性系统的物理信息神经微分方程框架（SPIN-ODE）。该方法采用三阶段优化流程：首先训练黑箱神经微分方程以拟合浓度轨迹；其次预训练化学反应神经网络（CRNN）学习浓度与其时间导数的映射关系；最后将速率系数与预训练的CRNN结合进行微调。在合成数据及新提出的实际数据集上的大量实验验证了本方法的有效性与鲁棒性。作为首个针对刚性神经微分方程的化学速率系数发现工作，本研究为神经网络与详细化学的融合提供了新方向。
原文摘要 · Abstract (English)
Estimating rate coefficients from complex chemical reactions is essential for advancing detailed chemistry. However, the stiffness inherent in real-world atmospheric chemistry systems poses severe challenges, leading to training instability and poor convergence, which hinder effective rate coefficient estimation using learning-based approaches. To address this, we propose a Stiff Physics-Informed Neural ODE framework (SPIN-ODE) for chemical reaction modelling. Our method introduces a three-stage optimisation process: first, a black-box neural ODE is trained to fit concentration trajectories; second, a Chemical Reaction Neural Network (CRNN) is pre-trained to learn the mapping between concentrations and their time derivatives; and third, the rate coefficients are fine-tuned by integrating with the pre-trained CRNN. Extensive experiments on both synthetic and newly proposed real-world datasets validate the effectiveness and robustness of our approach. As the first work addressing stiff neural ODE for chemical rate coefficient discovery, our study opens promising directions for integrating neural networks with detailed chemistry.
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