Journal of Computational and Applied Mathematics2022,Vol.41218.DOI:10.1016/j.cam.2022.114342

Spatial resolution of different discretizations over long-time for the Dirac equation with small potentials

Feng, Yue Yin, Jia
Journal of Computational and Applied Mathematics2022,Vol.41218.DOI:10.1016/j.cam.2022.114342

Spatial resolution of different discretizations over long-time for the Dirac equation with small potentials

Feng, Yue 1Yin, Jia1
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作者信息

  • 1. Natl Univ Singapore
  • 折叠

Abstract

We compare the long-time error bounds and spatial resolution of finite difference methods with different spatial discretizations for the Dirac equation with small electromagnetic potentials characterized by epsilon is an element of (0, 1] a dimensionless parameter. We begin with the simple and widely used finite difference time domain (FDTD) methods, and establish rigorous error bounds of them, which are valid up to the time at O(1/epsilon). In the error estimates, we pay particular attention to how the errors depend explicitly on the mesh size h and time step r as well as the small parameter epsilon. Based on the results, in order to obtain "correct "numerical solutions up to the time at O(1/epsilon), the epsilon-scalability (or meshing strategy requirement) of the FDTD methods should be taken as h = O(epsilon(1/2)) and r = O(epsilon(1/2)). To improve the spatial resolution capacity, we apply the Fourier spectral method to discretize the Dirac equation in space. Error bounds of the resulting finite difference Fourier pseudospectral (FDFP) methods show that they exhibit uniform spatial errors in the long-time regime, which are optimal in space as suggested by the Shannon's sampling theorem. Extensive numerical results are reported to confirm the error bounds and demonstrate that they are sharp. Published by Elsevier B.V.

Key words

Dirac equation/Long-time dynamics/Finite difference method/Spectral method/epsilon-scalability/NUMERICAL-METHODS/SEMICLASSICAL ASYMPTOTICS/SCHRODINGER-EQUATION/SPLITTING METHODS/MAXWELL/APPROXIMATIONS/LIMIT

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出版年

2022
Journal of Computational and Applied Mathematics

Journal of Computational and Applied Mathematics

EISCI
ISSN:0377-0427
被引量9
参考文献量48
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