A fast finite difference method for distributed-order space-fractional partial differential equations on convex domains. (15th March 2018)

• Record Type: Journal Article
• Authors: Jia, Jinhong; Wang, Hong
• Is Part Of: Computers & mathematics with applications. Volume 75:issue 6(2018)
• Page Start: 2031
• Page End: 2043
• Publication Date: 2018-03-15
• Subjects: Finite difference method for fractional differential equation on convex domain -- Multi-term fractional differential equation -- Distributed-order fractional differential equation; Electronic data processing -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.28541
• DOI: 10.1016/j.camwa.2017.09.003
• Languages: English
• ISSNs: 0898-1221

Abstract

Abstract: Fractional partial differential equations (PDEs) provide a powerful and flexible tool for modeling challenging phenomena including anomalous diffusion processes and long-range spatial interactions, which cannot be modeled accurately by classical second-order diffusion equations. However, numerical methods for space-fractional PDEs usually generate dense or full stiffness matrices, for which a direct solver requires O ( N 3 ) computations per time step and O ( N 2 ) memory, where N is the number of unknowns. The significant computational work and memory requirement of the numerical methods makes a realistic numerical modeling of three-dimensional space-fractional diffusion equations computationally intractable. Fast numerical methods were previously developed for space-fractional PDEs on multidimensional rectangular domains, without resorting to lossy compression, but rather, via the exploration of the tensor-product form of the Toeplitz-like decompositions of the stiffness matrices. In this paper we develop a fast finite difference method for distributed-order space-fractional PDEs on a general convex domain in multiple space dimensions. The fast method has an optimal order storage requirement and almost linear computational complexity, without any lossy compression. Numerical experiments show the utility of the method.