Semi-analytical algorithm for quasicrystal patterns

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Keyue Sun, Xiangjie Kong, Junxiang Yang
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引用次数: 0

Abstract

To efficiently simulate the quasicrystal patterns, we present a multi-stage semi-analytically algorithm. Utilizing the operator splitting strategy, we first split the original equation into three subproblems. A second-order five-stage scheme consists of solving four nonlinear ordinary differential equations with half time step and solving a linear partial differential equation with full time step. Using the methods of separation of variables, the nonlinear ODEs have analytical solutions. The linear PDE can also be analytically solved by using the Fourier-spectral method in space. In this sense, our proposed is semi-analytical because we only adopt an approximation in time. In each time step, we only need to compute several analytically solutions in a step-by-step manner. Therefore, the algorithm will be highly efficient and the simulation can be easily implemented. The performance and high efficiency of our proposed algorithm are verified via several simulations. To facilitate the interested readers to develop related researches, a MATLAB code for generating 12-fold quasicrystal patterns is provided in Appendix. We also share the computational code on Code Ocean platform, please refer to https://doi.org/10.24433/CO.6028082.v1.
准晶图的半解析算法
为了有效地模拟准晶图案,我们提出了一种多阶段半解析算法。利用算子拆分策略,首先将原方程拆分为三个子问题。二阶五阶段格式包括求解四个半时间步长非线性常微分方程和求解一个全时间步长线性偏微分方程。利用分离变量的方法,得到了非线性微分方程的解析解。线性偏微分方程也可以用空间傅里叶谱法解析求解。从这个意义上说,我们的建议是半解析的,因为我们只采用时间上的近似。在每个时间步中,我们只需要一步一步地计算几个解析解。因此,该算法效率高,且易于实现仿真。通过仿真验证了该算法的性能和高效性。为了方便有兴趣的读者开展相关研究,附录中提供了生成12重准晶图的MATLAB代码。我们也在code Ocean平台上分享计算代码,请参考https://doi.org/10.24433/CO.6028082.v1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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