Solving Multi-Term Time-Fractional Diffusion Equations Using a High-Order Fractional Finite Difference Scheme Accompanied by Neural Network Techniques

This article is devoted to solving multi-term time-fractional diffusion equations with a high-order finite difference scheme. We approximate the temporal fractional derivatives by the L1-2-3 formula, an expansion of the L1 formula with higher accuracy. We employ neural network strategies and automatic differentiation techniques for approximating integer-order derivatives. In the present work, we propose very important lemmas and theorems to prove the unconditional stability and convergence of the proposed scheme. Extensive numerical experiments for one- and two-dimensional problems, especially for curved boundaries, confirm the theoretical convergence orders. Comparisons with fractional physics-informed neural networks and finite difference methods in solving the multi-term time-fractional diffusion equations demonstrate the superiority of the proposed method. It is also illustrated that the proposed scheme can efficiently and accurately extract the pattern of the solutions even when noise corruption up to ten percent is imposed on the forcing term.