Coupled-map lattice model for crystal growth

1990 ◽  
Vol 42 (10) ◽  
pp. 6125-6128 ◽  
Author(s):  
David A. Kessler ◽  
Herbert Levine ◽  
W. N. Reynolds
2020 ◽  
Vol 17 (168) ◽  
pp. 20200187 ◽  
Author(s):  
Silvana S. S. Cardoso ◽  
Julyan H. E. Cartwright ◽  
Antonio G. Checa ◽  
Bruno Escribano ◽  
Antonio J. Osuna-Mascaró ◽  
...  

Stingless bees of the genus Tetragonula construct a brood comb with a spiral or a target pattern architecture in three dimensions. Crystals possess these same patterns on the molecular scale. Here, we show that the same excitable-medium dynamics governs both crystal nucleation and growth and comb construction in Tetragonula , so that a minimal coupled-map lattice model based on crystal growth explains how these bees produce the structures seen in their bee combs.


2010 ◽  
Vol 24 (28) ◽  
pp. 5495-5503
Author(s):  
SHUTANG LIU ◽  
FUYAN SUN ◽  
JIE SUN

This article summarizes the uniformity law of spatial physics kinematic systems, and studies the chaos and bifurcation behavior of the uniform system in space. In particular, it also fully explains the relation among the uniform system, the coupled map lattice model which has attracted considerable interest currently, and one-dimensional nonlinear dynamical systems.


Pramana ◽  
2002 ◽  
Vol 59 (1) ◽  
pp. 91-111
Author(s):  
G Balasubramanian ◽  
DJ Olinger ◽  
MA Demetriou

2010 ◽  
Vol 20 (07) ◽  
pp. 2137-2150 ◽  
Author(s):  
YUZHU GUO ◽  
YIFAN ZHAO ◽  
S. A. BILLINGS ◽  
DANIEL COCA ◽  
R. I. RISTIC ◽  
...  

The identification problem for excitable media is investigated in this paper. A new scalar coupled map lattice (SCML) model is introduced and the orthogonal least squares algorithm is employed to determinate the structure of the SCML model and to estimate the associated parameters. A simulated pattern and a pattern observed directly from a real Belousov–Zhabotinsky reaction are identified. The identified SCML models are shown to possess almost the same local dynamics as the original systems and are able to provide good long term predictions.


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