Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences· 2026Q1
Turing diffusion-driven instability analysis of a three-component activator-bi-inhibitor reaction–diffusion system with linear cross-diffusion
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- 2026year
Short summary
A new method derives 17 sets of conditions for diffusion-driven instability in a three-component reaction-diffusion system, a significant increase from the single condition in two-component systems.
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Key points
- Derived 17 sets of necessary conditions for diffusion-driven instability in a three-component reaction-diffusion system.
- Instability conditions arise from violating Routh-Hurwitz criteria for stability.
- The analysis involves studying quadratic and cubic polynomials of wave numbers, unlike the single quadratic polynomial in two-component systems.
- Sufficient conditions for pattern formation were established by isolating specific modes within derived parameter spaces.
- Finite element simulations confirmed the theoretical predictions of linear stability analysis.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract This article presents a systematic approach for deriving the necessary and sufficient conditions for diffusion-driven instability for a three-component reaction–diffusion system (RDS) with linear cross-diffusion and activator-bi-inhibitor reaction kinetics. First, by considering the system in the absence of spatial variations, we derive the necessary conditions for the uniform steady state to be linearly asymptotically stable, based on the Routh–Hurwitz criterion. Second, by introducing spatial variations, we derive 17 different sets of necessary conditions under which the uniform steady state becomes linearly unstable. These conditions emerge from the violation of at least one of the Routh–Hurwitz conditions for stability. The derivation of these conditions reduces to studying one quadratic and two cubic polynomials as functions of the wave numbers. This is in contrast to the case of a two-component RDS, where Turing instability is driven by a single quadratic polynomial. To verify the validity of these conditions, we systematically derive diffusion-driven instability parameter spaces by picking cross-diffusion tensor matrices from real physical systems. Sufficient conditions are established by determining parameter values from the generated parameter spaces such that specific modes are isolated for pattern formation. Finite element simulations in 1–space dimension confirm theoretical predictions of linear stability analysis.
The authors' abstract, as published at the source. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 2026 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science