Efficient Multistep Methods for Tempered Fractional Calculus: Algorithms and Simulations
Author(s) -
Ling Guo,
Fanhai Zeng,
Ian Turner,
Kevin Burrage,
George Em Karniadakis
Publication year - 2019
Publication title -
siam journal on scientific computing
Language(s) - English
Resource type - Journals
eISSN - 1095-7197
pISSN - 1064-8275
DOI - 10.1137/18m1230153
Subject(s) - mathematics , fractional calculus , convolution (computer science) , operator (biology) , algorithm , trapezoidal rule , quadrature (astronomy) , mathematical analysis , numerical integration , computer science , biochemistry , chemistry , repressor , machine learning , artificial neural network , transcription factor , electrical engineering , gene , engineering
In this work, we extend the fractional linear multistep methods in [C. Lubich, SIAM J. Math. Anal., 17 (1986), pp.704--719] to the tempered fractional integral and derivative operators in the sense that the tempered fractional derivative operator is interpreted in terms of the Hadamard finite-part integral. We develop two fast methods, Fast Method I and Fast Method II, with linear complexity to calculate the discrete convolution for the approximation of the (tempered) fractional operator. Fast Method I is based on a local approximation for the contour integral that represents the convolution weight. Fast Method II is based on a globally uniform approximation of the trapezoidal rule for the integral on the real line. Both methods are efficient, but numerical experimentation reveals that Fast Method II outperforms Fast Method I in terms of accuracy, efficiency, and coding simplicity. The memory requirement and computational cost of Fast Method II are $O(Q)$ and $O(Qn_T)$, respectively, where $n_T$ is the number of the final time steps and $Q$ is the number of quadrature points used in the trapezoidal rule. The effectiveness of the fast methods is verified through a series of numerical examples for long-time integration, including a numerical study of a fractional reaction-diffusion model.
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