Applications of Wavelet Bases to The Numerical Solutions of Fractional PDEs

TL;DR

This paper explores the use of wavelet bases for numerically solving fractional PDEs, highlighting benefits like preconditioning, adaptivity, and structure preservation, with efficient matrix generation and multilevel methods.

Contribution

It introduces efficient techniques for generating stiffness matrices, discusses effective preconditioners and multigrid methods, and details wavelet adaptivity for fractional PDEs.

Findings

Efficient matrix generation with computational cost O(2^J)

Effective preconditioners for time-independent equations

Wavelet adaptivity improves solving fractional PDEs

Abstract

For describing the probability distribution of the positions and times of particles performing anomalous motion, fractional PDEs are derived from the continuous time random walk models with waiting time distribution having divergent first order moment and/or jump length distribution which has divergent second order moment. It can be noted that the fractional PDEs are essentially dealing with the multiscale issues. Generally the regularity of the solutions for fractional PDEs is weak at the areas close to boundary and initial time. This paper focuses on developing the applications of wavelet bases to numerically solving fractional PDEs and digging out the potential benefits of wavelet methods comparing with other numerical methods, especially in the aspects of realizing preconditioning, adaptivity, and keeping the Toeplitz structure. More specifically, the contributions of this paper are as follows: 1. the techniques of efficiently generating stiffness matrix with computational cost $\mathcal{O}(2^J)$ are provided for first, second, and any order bases; 2. theoretically and numerically discuss the effective preconditioner for time-independent equation and multigrid method for time-dependent equation, respectively; 3. the wavelet adaptivity is detailedly discussed and numerically applied to solving the time-dependent (independent) equations. In fact, having reliable, simple, and local regularity indicators is the striking benefit of the wavelet in adaptively solving fractional PDEs (it seems hard to give a local posteriori error estimate for the adaptive finite element method because of the global property of the operator).