现代信息科技2025，Vol.9Issue(11)：25-32,37,9.DOI:10.19850/j.cnki.2096-4706.2025.11.006
HT-PeRCNN:基于Hessian矩阵迹权重的物理编码递归卷积神经网络
HT-PeRCNN:Physics-encoded Recurrent Convolutional Neural Network Based on Hessian Trace Weight
伏倩1
作者信息
- 1. 中国石油化工股份有限公司胜利油田分公司滨南采油厂 信息化服务中心,山东 滨州 256600
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摘要
Abstract
In recent years,Physics-Informed Neural Networks(PINNs)have achieved significant progress as a Deep Learning-based approach for solving Partial Differential Equations(PDEs)in various fields.However,PINNs still suffer from problems of low training efficiency and slow inference speed.To address these problems,this study proposes an improved method of Physics-encoded Recurrent Convolutional Neural Network(PeRCNN)based on the Hessian trace weight,named HT-PeRCNN.The method uses the Hessian trace as a weighted factor to optimize the weighted distribution of loss function,enhancing model stability and extrapolation capability.Experimental results show that HT-PeRCNN improves solution accuracy by 50%compared to PeRCNN in multiple PDE-solving tasks.关键词
偏微分方程/物理信息神经网络/物理编码递归卷积神经网络/Hessian矩阵迹权重Key words
PDE/PINN/PeRCNN/Hessian trace weight分类
信息技术与安全科学引用本文复制引用
伏倩..HT-PeRCNN:基于Hessian矩阵迹权重的物理编码递归卷积神经网络[J].现代信息科技,2025,9(11):25-32,37,9.
