A Novel and Simple Spectral Method for Nonlocal Pdes with the Fractional Laplacian
Abstract
We propose a novel and simple spectral method based on the semi-discrete Fourier transforms to discretize the fractional Laplacian [Formula presented]. Numerical analysis and experiments are provided to study its performance. Our method has the same symbol |ξ|α as the fractional Laplacian [Formula presented] at the discrete level, and thus it can be viewed as the exact discrete analogue of the fractional Laplacian. This unique feature distinguishes our method from other existing methods for the fractional Laplacian. Note that our method is different from the Fourier pseudospectral methods in the literature which are usually limited to periodic boundary conditions (see Remark 1.1). Numerical analysis shows that our method can achieve a spectral accuracy. The stability and convergence of our method in solving the fractional Poisson equations were analyzed. Our scheme yields a multilevel Toeplitz stiffness matrix, and thus fast algorithms can be developed for efficient matrix-vector multiplications. The computational complexity is O(2Nlog(2N)), and the memory storage is O(N) with N the total number of points. Extensive numerical experiments verify our analytical results and demonstrate the effectiveness of our method in solving various problems.
Recommended Citation
S. Zhou and Y. Zhang, "A Novel and Simple Spectral Method for Nonlocal Pdes with the Fractional Laplacian," Computers and Mathematics with Applications, vol. 168, pp. 133 - 147, Elsevier, Aug 2024.
The definitive version is available at https://doi.org/10.1016/j.camwa.2024.06.001
Department(s)
Mathematics and Statistics
Keywords and Phrases
Anomalous diffusion; Fractional Laplacian; Fractional Poisson equations; Hypergeometric functions; Semi-discrete Fourier transform; Spectral methods
International Standard Serial Number (ISSN)
0898-1221
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
15 Aug 2024
Comments
National Science Foundation, Grant DMS–1913293