Edge rewiring for network Turing patterns
Abstract
Extending a framework originally proposed by Cencetti et al. [Eur. Phys. J. B 90, 1 (2017)], we investigate a topological control mechanism for mediating Turing patterns on unweighted, undirected networks. Our primary contribution is proving boundedness results involved in targeted destabilization, in which we map Laplacian mode shifts to structural interventions. Through numerical simulations, we show the efficacy of this control scheme on a range of graph models, establishing theoretical expectations for the special case of the ring lattice. This work stands against a backdrop of real-world applications, moving the needle toward better understanding and engineering of network-driven pattern formation.
Fields of Study & Subjects
Nonlinear Dynamics and Pattern Formation
Neural Networks and Reservoir Computing
Neural Networks Stability and Synchronization
Publication details
- Journal / source
- Chaos An Interdisciplinary Journal of Nonlinear Science
- Publisher
- AIP Publishing
- Volume
- 36
- Issue
- 1
- Publication date
- 1 January 2026
- Language
- EN