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Symmetry
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26 December 2025

On Fractional Partial Differential Systems with Incommensurate Orders: Stability Analysis of Some Reaction–Diffusion Models

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1
Department of Electronics Engineering, Applied College, University of Ha’il, Ha’il 2440, Saudi Arabia
2
Laboratory of Dynamical Systems and Control, Department of Mathematics and Computer Science, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria
3
Department of Mathematics and Computer Science, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria
4
Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
Symmetry2026, 18(1), 52;https://doi.org/10.3390/sym18010052 
(registering DOI)
This article belongs to the Special Issue Symmetry in Fractional Derivatives, Fractional Equations and Fractional Order Systems

Abstract

This work develops and analyzes an incommensurate fractional FitzHugh–Nagumo (FHN) reaction–diffusion system in which each state variable evolves with a distinct fractional order. The formulation extends the classical and commensurate fractional models by incorporating heterogeneous memory effects that break temporal symmetry between the activator and inhibitor variables. After establishing the mathematical framework, the equilibrium states of the system are derived and subjected to a detailed local stability analysis in both diffusion-free and diffusion-driven regimes. Explicit stability criteria are obtained by examining the spectral properties of the linearized operator under incommensurate fractional dynamics. Numerical simulations based on a Caputo L1 discretization scheme corroborate the theoretical results and demonstrate how asymmetric memory orders influence transient behavior, convergence rates, and the qualitative structure of the solutions. The study provides the first systematic stability characterization of an incommensurate fractional FitzHugh–Nagumo reaction–diffusion model, highlighting the role of fractional-order asymmetry in shaping the system’s dynamical response.

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