Data-Driven Ai-and Bi-Soliton of the Cylindrical Korteweg-de Vries Equation via Prior-Information Physics-Informed Neural Networks  

作　　者：田十方 李彪 张钊 Shifang Tian;Biao Li;Zhao Zhang(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)

机构地区：[1]School of Mathematics and Statistics,Ningbo University,Ningbo 315211,China [2]Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices,South China Normal University,Guangzhou 510631,China

出　　处：《Chinese Physics Letters》2024年第3期1-6,共6页中国物理快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12175111 and 12235007);the K.C.Wong Magna Fund in Ningbo University。

摘　　要：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.

关 键 词：equation SOLITON CYLINDRICAL 

分 类 号：O175[理学—数学]