广东工业大学学报2026，Vol.43Issue(2)：41-51,11.DOI:10.12052/gdutxb.240152
具守恒量波动方程的循环双子网物理信息神经网络
The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities
摘要
Abstract
Physics-Informed Neural Networks(PINNs)have demonstrated significant potential in solving partial differential equations(PDEs)and modeling complex physical systems.However,when dealing with multi-scale,multi-domain scenarios and multi-physics coupled systems,PINNs face challenges such as low training efficiency and optimization instability.Based on existing PINN methods,a conservation-based Cross-Twin Network(CTN)approach is proposed for solving wave equations.By introducing interactive information-sharing and constraint mechanisms,the proposed method significantly improves the convergence speed,prediction accuracy,and training stability in multi-domain and multi-scale scenarios.Experimental results show that,compared with traditional methods,the Cross-Twin Network achieves superior performance in solving nonlinear higher-order wave PDEs and equation systems.This study provides new insights for the research and application of PINNs.关键词
物理信息神经网络/偏微分方程/波动方程/循环双子网络/守恒律Key words
physics-informed neural network/partial differential equations/wave equation/cross-twin network/conservation laws分类
信息技术与安全科学引用本文复制引用
李剑豪,房金伟..具守恒量波动方程的循环双子网物理信息神经网络[J].广东工业大学学报,2026,43(2):41-51,11.基金项目
国家自然科学基金青年基金资助项目(12001115) （12001115）
广州市科技计划项目(202201010648) （202201010648）
