化学工程2024，Vol.52Issue(7)：77-81,94,6.DOI:10.3969/j.issn.1005-9954.2024.07.013
基于物理信息机器学习的酶促反应系统参数估计
Parameter estimation of enzymatic reaction systems based on physics-informed machine learning
摘要
Abstract
To reveal the potential of physics-informed neural networks in biochemistry,a new parameter estimation method based on modem physics-informed machine learning tools was investigated and its function was demonstrated through a case study of enzymatic synthesis process and the effects of soft and hard boundary constraint settings were compared on the computational results.The experimental results show that both physics-informed neural networks with soft and hard constraints can accurately estimate model parameters,with goodness of fit R2 above 0.98 on all observable variables.The resulting system model can better reflect the dynamic process of the system.The proposed method combines the advantages of model-driven and data-driven approaches and achieves robust training results on a small dataset based on 40 noisy samples,significantly reducing the required data.关键词
物理信息嵌入/酶促反应/神经网络/参数估计/硬约束Key words
physics-informed/enzyme reaction/neural network/parameter estimation/hard constraint分类
信息技术与安全科学引用本文复制引用
刘承杰,俞辉,陈宇,戴厚德..基于物理信息机器学习的酶促反应系统参数估计[J].化学工程,2024,52(7):77-81,94,6.基金项目
福建省特种智能装备安全与测控重点实验室(FJIES2023KF02) （FJIES2023KF02）
泉州市科技计划项目(2022C004L) （2022C004L）
