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HIGH-ORDER SPLITTING FINITE ELEMENT METHODS FOR THE SUBDIFFUSION EQUATION WITH LIMITED SMOOTHING PROPERTY

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

In contrast with the diffusion equation which smoothens the initial data to C∞ for t?> 0 (away from the corners/edges of the domain), the subdiffusion equation only exhibits limited spatial regularity. As a result, one generally cannot expect high-order accuracy in space in solving the subdiffusion equation with nonsmooth initial data. In this paper, a new splitting of the solution is constructed for high-order finite element approximations to the subdiffusion equation with nonsmooth initial data. The method is constructed by splitting the solution into two parts, i.e., a time-dependent smooth part and a time-independent nonsmooth part, and then approximating the two parts via different strategies. The time-dependent smooth part is approximated by using high-order finite element method in space and convolution quadrature in time, while the steady nonsmooth part could be approximated by using smaller mesh size or other methods that could yield high-order accuracy. Several examples are presented to show how to accurately approximate the steady nonsmooth part, including piecewise smooth initial data, Dirac–Delta point initial data, and Dirac measure concentrated on an interface. The argument could be directly extended to subdiffusion equations with nonsmooth source data. Extensive numerical experiments are presented to support the theoretical analysis and to illustrate the performance of the proposed high-order splitting finite element methods.

Original languageEnglish
Pages (from-to)2557-2586
Number of pages30
JournalMathematics of Computation
Volume93
Issue number350
DOIs
Publication statusPublished - Nov 2024

Keywords

  • convolution quadrature
  • error estimate
  • finite element method
  • high-order
  • limited smoothing property
  • nonsmooth data
  • Subdiffusion

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Computational Mathematics
  • Applied Mathematics

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