A sparse spectral method for fractional differential equations in one-spatial dimension

TL;DR

This paper introduces a novel sparse spectral method for efficiently solving fractional differential equations in one dimension, leveraging weighted Chebyshev polynomials and their Hilbert transforms to achieve linear complexity solutions.

Contribution

The paper develops a sparse spectral approach that decouples fractional differential operators across affine transformations, enabling efficient independent linear system solutions.

Findings

Linear complexity solve for fractional equations

Sparse linear systems with $ ext{O}(n)$ nonzero entries

Application to fractional heat and wave equations

Abstract

We develop a sparse spectral method for a class of fractional differential equations, posed on $\mathbb{R}$, in one dimension. These equations can include sqrt-Laplacian, Hilbert, derivative and identity terms. The numerical method utilizes a basis consisting of weighted Chebyshev polynomials of the second kind in conjunction with their Hilbert transforms. The former functions are supported on $[-1,1]$ whereas the latter have global support. The global approximation space can contain different affine transformations of the basis, mapping $[-1,1]$ to other intervals. Remarkably, not only are the induced linear systems sparse, but the operator decouples across the different affine transformations. Hence, the solve reduces to solving $K$ independent sparse linear systems of size $\mathcal{O}(n)\times \mathcal{O}(n)$, with $\mathcal{O}(n)$ nonzero entries, where $K$ is the number of different intervals and $n$ is the highest polynomial degree contained in the sum space. This results in an $\mathcal{O}(n)$ complexity solve. Applications to fractional heat and wave equations are considered.