Abstract
| Original language | English |
|---|---|
| Article number | 148301 |
| Pages (from-to) | 148301-1 148301-5 |
| Number of pages | 5 |
| Journal | Physical review letters |
| Volume | 119 |
| Issue number | 14 |
| DOIs | |
| Publication status | Published - 4 Oct 2017 |
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Keywords
- nonlinear dynamics
- time varying networks
- pattern formation
- Turing patterns
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Theory of Turing Patterns on Time Varying Networks. / Petit, Julien; Lauwens, Ben; Fanelli, Duccio; Carletti, Timoteo.
In: Physical review letters, Vol. 119, No. 14, 148301, 04.10.2017, p. 148301-1 148301-5.Research output: Contribution to journal › Letter
TY - JOUR
T1 - Theory of Turing Patterns on Time Varying Networks
AU - Petit, Julien
AU - Lauwens, Ben
AU - Fanelli, Duccio
AU - Carletti, Timoteo
PY - 2017/10/4
Y1 - 2017/10/4
N2 - The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.
AB - The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.
KW - nonlinear dynamics
KW - time varying networks
KW - pattern formation
KW - Turing patterns
U2 - https://doi.org/10.1103/PhysRevLett.119.148301
DO - https://doi.org/10.1103/PhysRevLett.119.148301
M3 - Letter
VL - 119
SP - 148301-1 148301-5
JO - Physical review letters
JF - Physical review letters
SN - 0031-9007
IS - 14
M1 - 148301
ER -