摘要
It is well-known that reaction–diffusion systems are used to describe the pattern formation models. In this paper,we will investigate the pattern formation generated by the fractional reaction–diffusion systems. We first explore the mathematical mechanism of the pattern by applying the linear stability analysis for the fractional Gierer–Meinhardt system.Then, an efficient high-precision numerical scheme is used in the numerical simulation. The proposed method is based on an exponential time differencing Runge–Kutta method in temporal direction and a Fourier spectral method in spatial direction. This method has the advantages of high precision, better stability, and less storage. Numerical simulations show that the system control parameters and fractional order exponent have decisive influence on the generation of patterns. Our numerical results verify our theoretical results.
It is well-known that reaction–diffusion systems are used to describe the pattern formation models. In this paper,we will investigate the pattern formation generated by the fractional reaction–diffusion systems. We first explore the mathematical mechanism of the pattern by applying the linear stability analysis for the fractional Gierer–Meinhardt system.Then, an efficient high-precision numerical scheme is used in the numerical simulation. The proposed method is based on an exponential time differencing Runge–Kutta method in temporal direction and a Fourier spectral method in spatial direction. This method has the advantages of high precision, better stability, and less storage. Numerical simulations show that the system control parameters and fractional order exponent have decisive influence on the generation of patterns. Our numerical results verify our theoretical results.
作者
Yu Wang
Rongpei Zhang
Zhen Wang
Zijian Han
王语;张荣培;王震;韩子健(School of Mathematics and Systematic Sciences, Shenyang Normal University;College of Mathematics and Systems Science, Shandong University of Science and Technology)
基金
Project supported by the National Natural Science Foundation of China(Grant Nos.61573008 and 61703290)
Natural Science Foundation of Liaoning Province,China(Grant No.20180550996)
