Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101
Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem,
especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a
physical information neural network that successfully obtains the time-varying parameters of differential equations.
In 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 networks. In this paper, taking
the electrophysiological differential… More >
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