Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network
This work addresses numerical solutions for time-fractional equations, which is an incremental advancement in computational methods for specific mathematical problems.
The paper tackled solving linear and nonlinear time-fractional partial integro-differential equations by proposing a novel machine learning method using an adaptive tensor neural network subspace combined with Gauss-Jacobi quadrature, with numerical examples validating its efficiency and accuracy.
In this paper, we propose a novel machine learning method based on adaptive tensor neural network subspace to solve linear time-fractional diffusion-wave equations and nonlinear time-fractional partial integro-differential equations. In this framework, the tensor neural network and Gauss-Jacobi quadrature are effectively combined to construct a universal numerical scheme for the temporal Caputo derivative with orders spanning $ (0,1)$ and $(1,2)$. Specifically, in order to effectively utilize Gauss-Jacobi quadrature to discretize Caputo derivatives, we design the tensor neural network function multiplied by the function $t^μ$ where the power $μ$ is selected according to the parameters of the equations at hand. Finally, some numerical examples are provided to validate the efficiency and accuracy of the proposed tensor neural network based machine learning method.