  In this article, a new deep learning architecture, named JDNN, has been
proposed to approximate a numerical solution to Partial Differential Equations
(PDEs). The JDNN is capable of solving high-dimensional equations. Here, Jacobi
Deep Neural Network (JDNN) has demonstrated various types of telegraph
equations. This model utilizes the orthogonal Jacobi polynomials as the
activation function to increase the accuracy and stability of the method for
solving partial differential equations. The finite difference time
discretization technique is used to overcome the computational complexity of
the given equation. The proposed scheme utilizes a Graphics Processing Unit
(GPU) to accelerate the learning process by taking advantage of the neural
network platforms. Comparing the existing methods, the numerical experiments
show that the proposed approach can efficiently learn the dynamics of the
physical problem.
