Numerical Study of Dynamical System Using Deep Learning Approach  

作　　者：Manana Chumburidze Miranda Mnatsakaniani David Lekveishvili Nana Julakidze Manana Chumburidze;Miranda Mnatsakaniani;David Lekveishvili;Nana Julakidze(Department of Computer Technology and Mathematics, Akaki Tsereteli State University, Kutaisi, Georgia)

机构地区：[1]Department of Computer Technology and Mathematics, Akaki Tsereteli State University, Kutaisi, Georgia

出　　处：《Open Journal of Applied Sciences》2025年第2期425-432,共8页应用科学(英文)

摘　　要：This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs into a computationally numerical format has been used. In particular, for investigation mathematical models of the dynamical system of cancer cell invasion in inhomogeneous areas of human tissues have been considered. Neural operators were initially proposed to model the differential operator of PDEs. The GKNN mapping features between input data to the PDEs and their solutions have been constructed. The boundary integral method in combination with Green’s functions for a large number of boundary conditions is used. The tools applied in this development are based on the Fourier neural operators (FNOs), graph theory, theory elasticity, and singular integral equations.This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs into a computationally numerical format has been used. In particular, for investigation mathematical models of the dynamical system of cancer cell invasion in inhomogeneous areas of human tissues have been considered. Neural operators were initially proposed to model the differential operator of PDEs. The GKNN mapping features between input data to the PDEs and their solutions have been constructed. The boundary integral method in combination with Green’s functions for a large number of boundary conditions is used. The tools applied in this development are based on the Fourier neural operators (FNOs), graph theory, theory elasticity, and singular integral equations.

关 键 词：Deep Learning Graph Kernel Network Green’s Tensor 

分 类 号：O17[理学—数学]