The Hamiltonian structure and fast energy-preserving algorithms for the fractional Klein-Gordon equation. (1st May 2022)

• Record Type: Journal Article
• Authors: Fu, Yayun; Zhao, Yanmin; Hu, Dongdong
• Is Part Of: Computers & mathematics with applications. Volume 113(2022)
• Page Start: 86
• Page End: 102
• Publication Date: 2022-05-01
• Subjects: Fractional Klein-Gordon equation -- Hamiltonian system -- Averaged vector field method -- Point-wise error estimate; Electronic data processing -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.28541
• DOI: 10.1016/j.camwa.2022.03.022
• Languages: English
• ISSNs: 0898-1221

Abstract

Abstract: In this paper, an energy-preserving difference scheme is proposed for solving the space fractional Klein-Gordon equation based on the Hamiltonian form of the equation. First, we study some properties of the fractional Laplacian operator and reformulate the equation as an infinite-dimension canonical Hamiltonian system. Then, we use the fractional centered difference formula to discrete the equation and derive a semi-discrete Hamiltonian system that can conserve the semi-discrete energy. Subsequently, a fully-discrete scheme is obtained by applying the averaged vector field method to the semi-discrete system. The resulting scheme's unconditional point-wise error estimate is discussed by using the "cut-off" technique. Furthermore, a fast solver is presented to reduce the computational complexity of the scheme based on the fast Fourier transformation technique in practical computation. Finally, some numerical experiments are displayed to demonstrate the efficiency and conservation of the constructed scheme in long time simulation. Highlights: We reformulate the fractional Klein-Gordon equation as a canonical Hamiltonian system. We explore an energy-preserving scheme based on the AVF method to solve the fractional Klein-Gordon equation. Without any restriction on the grid ratio, the proposed scheme is convergent with order O ( h 2 + τ 2 ) in L ∞ -norm. A fast algorithm is used to reduce the computational complexity in practical computation.