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An analysis of the Crank-Nicolson method for subdiffusion

Jin, B; Li, B; Zhou, Z; (2018) An analysis of the Crank-Nicolson method for subdiffusion. IMA Journal of Numerical Analysis , 38 (1) pp. 518-541. 10.1093/imanum/drx019. Green open access

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Abstract

In this work, we analyse a Crank-Nicolson type time-stepping scheme for the subdiffusion equation, which involves a Caputo fractional derivative of order α∈(0,1) in time. It hybridizes the backward Euler convolution quadrature with a θ -type method, with the parameter θ dependent on the fractional order α by θ=α/2 and naturally generalizes the classical Crank–Nicolson method. We develop essential initial corrections at the starting two steps for the Crank–Nicolson scheme and, together with the Galerkin finite element method in space, obtain a fully discrete scheme. The overall scheme is easy to implement and robust with respect to data regularity. A complete error analysis of the fully discrete scheme is provided, and a second-order accuracy in time is established for both smooth and nonsmooth problem data. Extensive numerical experiments are provided to illustrate its accuracy, efficiency and robustness, and a comparative study also indicates its competitive with existing schemes.

Type: Article
Title: An analysis of the Crank-Nicolson method for subdiffusion
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/imanum/drx019
Publisher version: http://dx.doi.org/10.1093/imanum/drx019
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Crank–Nicolson method, subdiffusion, initial correction, error estimates, nonsmooth data, convolution quadrature
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1543177
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