首页|Data-Driven Ai-and Bi-Soliton of the Cylindrical Korteweg-de Vries Equation via Prior-Information Physics-Informed Neural Networks
Data-Driven Ai-and Bi-Soliton of the Cylindrical Korteweg-de Vries Equation via Prior-Information Physics-Informed Neural Networks
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
By the modifying loss function MSE and training area of physics-informed neural networks(PINNs),we propose a neural networks model,namely prior-information PINNs(PIPINNs).We demonstrate the advantages of PIPINNs by simulating Ai-and Bi-soliton solutions of the cylindrical Korteweg-de Vries(cKdV)equation.Numerical experiments show that our proposed model is able not only to simulate these solitons using the cKdV equation,but also to significantly improve its simulation capability.Compared with the original PINNs,the prediction accuracy of our proposed model is improved by one to three orders of magnitude.Moreover,the accuracy of the PIPINNs is further improved by adding the restriction of conservation of energy.
田十方、李彪、张钊
展开 >
School of Mathematics and Statistics,Ningbo University,Ningbo 315211,China
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices,South China Normal University,Guangzhou 510631,China
国家自然科学基金国家自然科学基金K.C.Wong Magna Foundation of Ningbo University
万方数据
维普
