Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification
Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification
复制标题
用于自主动力学不确定性量化的贝叶斯化学反应神经网络
DOI：
10.1039/d2cp05083h
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发表时间：
2023
影响因子：
3.3
通讯作者：
Deng, Sili
中科院分区：
文献类型：
--
作者：
Li, Qiaofeng;Chen, Huaibo;Koenig, Benjamin C.;Deng, Sili
化学反应神经网络（CRNN）是近年来发展起来的一种自主发现反应模型的工具，已成功地应用于各种化学工程和生化系统。它利用了现代深度神经网络（DNN）非凡的数据拟合能力，同时通过嵌入广泛适用的物理定律（如质量作用定律和阿克里先定律）来保持高度的可解释性和鲁棒性。在本文中，我们进一步发展贝叶斯CRNN，不仅重建，而且量化的不确定性的化学动力学模型的数据。采用马尔可夫链蒙特卡罗算法和变分推理两种方法实现贝叶斯CRNN，后者主要是因为其速度快。我们展示了贝叶斯CRNN在不同类型化学系统的动力学不确定性量化中的能力，并讨论了在数据驱动建模中嵌入物理定律的重要性。最后，我们讨论了贝叶斯CRNN的适应不完整的测量和模型混合的全球不确定性量化。
Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification.