Turing patterns on discrete topologies: from networks to higher-order structures
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914104558485504 |
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| author | Muolo, Riccardo Giambagli, Lorenzo Nakao, Hiroya Fanelli, Duccio Carletti, Timoteo |
| author_facet | Muolo, Riccardo Giambagli, Lorenzo Nakao, Hiroya Fanelli, Duccio Carletti, Timoteo |
| contents | Nature is a blossoming of regular structures, signature of self-organization of the underlying microscopic interacting agents. Turing theory of pattern formation is one of the most studied mechanisms to address such phenomena and has been applied to a widespread gallery of disciplines. Turing himself used a spatial discretization of the hosting support to eventually deal with a set of ODEs. Such an idea contained the seeds of the theory on discrete support, which has been fully acknowledged with the birth of network science in the early 2000s. This approach allows us to tackle several settings not displaying a trivial continuous embedding, such as multiplex, temporal networks, and, recently, higher-order structures. This line of research has been mostly confined within the network science community, despite its inherent potential to transcend the conventional boundaries of the PDE-based approach to Turing patterns. Moreover, network topology allows for novel dynamics to be generated via a universal formalism that can be readily extended to account for higher-order structures. The interplay between continuous and discrete settings can pave the way for further developments in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07663 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Turing patterns on discrete topologies: from networks to higher-order structures Muolo, Riccardo Giambagli, Lorenzo Nakao, Hiroya Fanelli, Duccio Carletti, Timoteo Pattern Formation and Solitons Statistical Mechanics Mathematical Physics Dynamical Systems Adaptation and Self-Organizing Systems Nature is a blossoming of regular structures, signature of self-organization of the underlying microscopic interacting agents. Turing theory of pattern formation is one of the most studied mechanisms to address such phenomena and has been applied to a widespread gallery of disciplines. Turing himself used a spatial discretization of the hosting support to eventually deal with a set of ODEs. Such an idea contained the seeds of the theory on discrete support, which has been fully acknowledged with the birth of network science in the early 2000s. This approach allows us to tackle several settings not displaying a trivial continuous embedding, such as multiplex, temporal networks, and, recently, higher-order structures. This line of research has been mostly confined within the network science community, despite its inherent potential to transcend the conventional boundaries of the PDE-based approach to Turing patterns. Moreover, network topology allows for novel dynamics to be generated via a universal formalism that can be readily extended to account for higher-order structures. The interplay between continuous and discrete settings can pave the way for further developments in the field. |
| title | Turing patterns on discrete topologies: from networks to higher-order structures |
| topic | Pattern Formation and Solitons Statistical Mechanics Mathematical Physics Dynamical Systems Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2407.07663 |