A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

作     者：Jiepeng Yao Zhanjia Peng Jingjing Liu Chengxiao Fan Zhongyi Wang Lan Huang 

作者机构：College of Information and Electrical EngineeringChina Agricultural UniversityBeijing100083China Key Laboratory of Agricultural Information Acquisition Technology(Beijing)Ministry of AgricultureBeijing100083China Key Laboratory of Modern Precision Agriculture System Integration ResearchMinistry of EducationBeijing100083China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第137卷第12期

页      面：2243-2265页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China under 62271488 and 61571443 

主　　题：Physics-informed neural network differential equation bioelectrical signals inverse problems 

摘      要：In the establishment of differential equations,the determination of time-varying parameters is a difficult problem,especially for equations related to life ***,we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential *** the proposed framework,the learnable factors and scale parameters are used to implement adaptive activation functions,and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural *** this paper,taking the electrophysiological differential equation as an example,the characteristic parameters of ion channel and pump kinetics are determined using *** results demonstrate that the numerical solution of the differential equation is calculated by the parameters predicted by BioE-PINN,the RootMean Square Error(RMSE)is between 0.01 and 0.3,and the Pearson coefficient is above 0.87,which verifies the effectiveness and accuracy of ***,realmeasuredmembrane potential data in animals and plants are employed to determine the parameters of the electrophysiological equations,with RMSE 0.02-0.2 and Pearson coefficient above *** conclusion,this framework can be applied not only for differential equation parameter determination of physiological processes but also the prediction of time-varying parameters of equations in other fields.