A Survey on Numerical Methods for Spectral Space-Fractional Diffusion Problems

TL;DR

This survey reviews numerical methods for solving spectral space-fractional diffusion equations, comparing their approaches, accuracy, and computational efficiency, with a focus on three main solution representations.

Contribution

It provides a comprehensive overview of existing numerical approaches for fractional diffusion problems, highlighting their theoretical foundations and practical performance.

Findings

All methods can be viewed as rational approximations of the fractional operator.

The paper compares the accuracy and efficiency of different numerical schemes.

Extension and spectral methods show promising robustness and computational performance.

Abstract

The survey is devoted to numerical solution of the fractional equation $A^\alpha u=f$, $0 < \alpha <1$, where $A$ is a symmetric positive definite operator corresponding to a second order elliptic boundary value problem in a bounded domain $\Omega$ in $\mathbb R^d$. The operator fractional power is a non-local operator and is defined through the spectrum. Due to growing interest and demand in applications of sub-diffusion models to physics and engineering, in the last decade, several numerical approaches have been proposed, studied, and tested. We consider discretizations of the elliptic operator $A$ by using an $N$-dimensional finite element space $V_h$ or finite differences over a uniform mesh with $N$ grid points. The numerical solution of this equation is based on the following three equivalent representations of the solution: (1) Dunford-Taylor integral formula (or its equivalent Balakrishnan formula), (2) extension of the a second order elliptic problem in $\Omega \times (0,\infty)\subset \mathbb R^{d+1}$ (with a local operator) or as a pseudo-parabolic equation in the cylinder $(x,t) \in \Omega \times (0,1) $, (3) spectral representation and the best uniform rational approximation (BURA) of $z^\alpha$ on $[0,1]$. Though substantially different in origin and their analysis, these methods can be interpreted as some rational approximation of $A^{-\alpha}$. In this paper we present the main ideas of these methods and the corresponding algorithms, discuss their accuracy, computational complexity and compare their efficiency and robustness.