Application of multivariate bilinear neural network method to fractional partial differential equations

被引：37

作者：

Liu, Jian-Guo ^{[1
]}

Zhu, Wen-Hui ^{[2
]}

Wu, Ya-Kui ^{[3
]}

Jin, Guo-Hua ^{[1
]}

机构：

[1] Jiangxi Univ Chinese Med, Coll Comp, Nanchang 330004, Jiangxi, Peoples R China

[2] Inst Artificial Intelligence, Nanchang Inst Sci & Technol, Nanchang 330108, Jiangxi, Peoples R China

[3] Jiujiang Univ, Sch Sci, Jiujiang 332005, Jiangxi, Peoples R China

来源：

RESULTS IN PHYSICS | 2023年 / 47卷

基金：

中国国家自然科学基金;

关键词：

Multivariate bilinear neural network method; Calogero-Bogoyavlensky-Schiff- Bogoyavlensky-Konopelchenko equation; Fractional; SOLITARY WAVE; LUMP;

D O I：

10.1016/j.rinp.2023.106341

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this work, a multivariate bilinear neural network method is proposed to seek more exact analytical solutions of nonlinear partial differential equations. As an example, the (2+1)-dimensional fractional generalized Calogero-Bogoyavlensky-Schiff-Bogoyavlensky-Konopelchenko equation is investigated via selecting the 3-2-2-1, 3-2-3-1 and 3-3-2-1 models, respectively. The exact analytical solutions with several arbitrary activation functions are derived and the dynamics properties are shown in some three-dimensional and density maps by choosing different activation functions.

引用

页数：5

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