@article{Li2026, 
author = {Jianhao Li and Jinwei Fang},
title = {The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities},
year = {2026},
journal = {Journal of Guangdong University of Technology},
volume = {43},
number = {2},
pages = {41-51},
keywords = {physics-informed neural network, partial differential equations, wave equation, cross-twin network, conservation laws},
url = {https://www.sciopen.com/article/10.12052/gdutxb.240152},
doi = {10.12052/gdutxb.240152},
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.}
}