Fast alternating-direction finite difference methods for three-dimensional space-fractional diffusion equations

2014 ◽  
Vol 258 ◽  
pp. 305-318 ◽  
Author(s):  
Hong Wang ◽  
Ning Du
Author(s):  
Yuki Takeuchi ◽  
Reiji Suda

Finite difference methods for fractional differential equation are ever proposed. However, precise error orders have not been analyzed for the methods higher than first order accuracy. This paper proposes a few finite difference methods for fractional diffusion equations and shows our methods have second order accuracy under the conditions that the solution functions have higher order than second order at boundaries. In addition, we show that the accuracy may decrease in the case that the solution functions have lower order than second order at boundaries when we use second order accuracy scheme. In this paper, we treat schemes based on Grunwald-Letnikov definition and apply them to three kinds of fractional diffusion equations using Riemann-Liouville derivative operator including time-fractional diffusion equation, space-fractional diffusion equation and time-space-fractional diffusion equation. Finally, we show the simulation results which indicate that our methods are stable and have successfully second order accuracy under the assumed conditions.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Rezvan Ghaffari ◽  
Farideh Ghoreishi

Abstract In this paper, we propose an improvement of the classical compact finite difference (CFD) method by using a proper orthogonal decomposition (POD) technique for time-fractional diffusion equations in one- and two-dimensional space. A reduced CFD method is constructed with lower dimensions such that it maintains the accuracy and decreases the computational time in comparison with classical CFD method. Since the solution of time-fractional diffusion equation typically has a weak singularity near the initial time t = 0 {t=0} , the classical L1 scheme on uniform meshes may obtain a scheme with low accuracy. So, we use the L1 scheme on graded meshes for time discretization. Moreover, we provide the error estimation between the reduced CFD method based on POD and classical CFD solutions. Some numerical examples show the effectiveness and accuracy of the proposed method.


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