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Reaction-diffusion Equations on Complex Networks and Turing Patterns, via p-Adic Analysis
Analysis of PDEs · Submitted 6 May 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Such behavior can be naturally explained in the p-adic continuous model, in a rigorous mathematical way, but not in the original discrete model.
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The abstract, sentence by sentence
Nakao and Mikhailov proposed using continuous models (mean-field models) to study reaction-diffusion systems on networks and the corresponding Turing patterns. This work aims to show that p-adic analysis is the natural tool to carry out this program. This is possible due to the fact that the discrete Laplacian attached to a network turns out to be a particular case of a p-adic Laplacian. By embedding the graph attached to the system into the field of p-adic numbers, we construct a continuous p-adic version of a reaction-diffusion system on a network. The existence and uniqueness of the Cauchy problems for these systems are established. We show that Turing criteria for the p-adic continuous reaction-diffusion system remain essentially the same as in the classical case. However, the properties of the emergent patterns are very different. The classical Turing patterns consisting of alternating domains do not occur in the p-adic continuous case. Instead of this, several domains (clusters) occur. Multistability, that is, coexistence of a number of different patterns with the same parameter values occurs. Clustering and multistability have been observed in the computer simulations of large reaction-diffusion systems on networks. Such behavior can be naturally explained in the p-adic continuous model, in a rigorous mathematical way, but not in the original discrete model.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Nakao and Mikhailov proposed using continuous models (mean-field models) to study reaction-diffusion systems on networks and the corresponding Turing patterns. | background 1.00 | 0.03 | 0.03 | asserted 0.99 |
| s1 | This work aims to show that p-adic analysis is the natural tool to carry out this program. | contribution 0.95 | 0.34 | 0.11 | asserted 0.96 |
| s2 | This is possible due to the fact that the discrete Laplacian attached to a network turns out to be a particular case of a p-adic Laplacian. | method 0.56 | 0.49 | 0.07 | asserted 0.99 |
| s3 | By embedding the graph attached to the system into the field of p-adic numbers, we construct a continuous p-adic version of a reaction-diffusion system on a network. | method 0.88 | 0.94 | 0.08 | asserted 1.00 |
| s4 | The existence and uniqueness of the Cauchy problems for these systems are established. | result 0.83 | 0.92 | 0.06 | asserted 1.00 |
| s5 | We show that Turing criteria for the p-adic continuous reaction-diffusion system remain essentially the same as in the classical case. | result 0.99 | 0.91 | 0.15 | asserted 1.00 |
| s6 | However, the properties of the emergent patterns are very different. | result 0.94 | 0.72 | 0.13 | asserted 1.00 |
| s7 | The classical Turing patterns consisting of alternating domains do not occur in the p-adic continuous case. | result 1.00 | 0.79 | 0.22 | asserted 1.00 |
| s8 | Instead of this, several domains (clusters) occur. | result 1.00 | 0.73 | 0.16 | asserted 1.00 |
| s9 | Multistability, that is, coexistence of a number of different patterns with the same parameter values occurs. | result 0.99 | 0.66 | 0.20 | asserted 1.00 |
| s10 | Clustering and multistability have been observed in the computer simulations of large reaction-diffusion systems on networks. | background 0.83 | 0.19 | 0.06 | asserted 1.00 |
| s11 | Such behavior can be naturally explained in the p-adic continuous model, in a rigorous mathematical way, but not in the original discrete model. | interpretation 0.90 | 0.71 | 0.48 | asserted 0.83 |
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Original abstract
Nakao and Mikhailov proposed using continuous models (mean-field models) to study reaction-diffusion systems on networks and the corresponding Turing patterns. This work aims to show that p-adic analysis is the natural tool to carry out this program. This is possible due to the fact that the discrete Laplacian attached to a network turns out to be a particular case of a p-adic Laplacian. By embedding the graph attached to the system into the field of p-adic numbers, we construct a continuous p-adic version of a reaction-diffusion system on a network. The existence and uniqueness of the Cauchy problems for these systems are established. We show that Turing criteria for the p-adic continuous reaction-diffusion system remain essentially the same as in the classical case. However, the properties of the emergent patterns are very different. The classical Turing patterns consisting of alternating domains do not occur in the p-adic continuous case. Instead of this, several domains (clusters) occur. Multistability, that is, coexistence of a number of different patterns with the same parameter values occurs. Clustering and multistability have been observed in the computer simulations of large reaction-diffusion systems on networks. Such behavior can be naturally explained in the p-adic continuous model, in a rigorous mathematical way, but not in the original discrete model.
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