Stability analysis of fractional two-dimensional reaction-diffusion model with applications in biological processes
Year: 2025
Type: article
Abstract: We study the stability of a two-dimensional fractional reaction-diffusion system under the Caputo differential operator in time. Our model is based on the Grey-Scott model, a well-known coupled reaction-diffusion system that describes the interaction between two chemical species. The diffusion term captures the species special spread, while the nonlinear term in the system describes the chemical reaction, resulting in a wide range of difficult, self-organizing patterns, including spots, stripes, or spirals, depending on the parameter values. We derive conditions for local stability of the homogeneous equilibrium by linearizing the system and analyzing the eigenvalues of the Jacobian. Furthermore, we construct appropriate Lyapunov functionals to establish global asymptotic stability of the discrete model under suitable conditions. This approach seeks to provide a robust framework for analyzing complex dynamical behaviors in systems governed by fractional-order in-time reaction-diffusion systems. The numerical simulations employ the Chebyshev spectral method for spatial discretization and the $ L1 $ scheme for fractional time derivatives. These simulations validate the theoretical findings, demonstrating the model's ability to replicate intricate patterns often observed in reaction-diffusion systems. The results suggest that the fractional-order framework enhances the understanding of pattern formation in such systems, making this model a valuable tool for studying anomalous diffusion and non-local dynamics in biological and chemical processes.
Source: AIMS Mathematics
DOI: 10.3934/math.2025531
Authors: Ishtiaq Ali, Saeed Islam
Institutions: King Faisal University, Prince Mohammad bin Fahd University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
Funder: King Faisal University
OpenAlex ID: W4410575931
