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Visualizzazione dei post in ordine di pertinenza per la query attractor. Ordina per data Mostra tutti i post
Visualizzazione dei post in ordine di pertinenza per la query attractor. Ordina per data Mostra tutti i post

sabato 12 gennaio 2019

# gst: how two chaotic systems can synchronize

<< For the first time the researchers were able to measure the fine grain process that leads from disorder to synchrony, discovering a new kind of synchronization between chaotic systems. They call this new phenomenon Topological Synchronization. >>

<< Chaotic systems, although unpredictable, still have a subtle global organization called strange attractor (..) Every chaotic system attracts its own unique strange attractor. By Topological Synchronization we mean that two strange attractors have the same organization and structures. At the beginning of the synchronization process, small areas on one strange attractor have the same structure of the other attractor, meaning that they are already synced to the other attractor. At the end of the process, all the areas of one strange attractor will have the structure of the other and complete Topological Synchronization has been reached. >> Nir Lahav.

Scientists reveal for first time the exact process by which chaotic systems synchronize. Bar-Ilan University. Jan 7, 2019.

https://m.phys.org/news/2019-01-scientists-reveal-exact-chaotic-synchronize.html

Nir Lahav, Irene Sendina-Nadal, et al.
Synchronization of chaotic systems: A microscopic description. Phys. Rev. E 98, 052204. Nov 6, 2018. doi: 10.1103/PhysRevE.98.052204

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.052204

sabato 17 dicembre 2022

# gst: transitions, how two saddles can increase the transient times.

FIG. 8. Attractor and chaotic saddles (..) amplified around three bands of the chaotic attractor.  The global chaotic saddle is colored blue, and the local chaotic saddle is colored red. The attractors are colored black. 

AA << consider a dissipative version of the standard nontwist map. Nontwist systems present a robust transport barrier, called the shearless curve, that becomes the shearless attractor when dissipation is introduced. This attractor can be regular or chaotic depending on the control parameters. Chaotic attractors can undergo sudden and qualitative changes as a parameter is varied. These changes are called crises, and at an interior crisis the attractor suddenly expands. Chaotic saddles are nonattracting chaotic sets that play a fundamental role in the dynamics of nonlinear systems, they are responsible for chaotic transients, fractal basin boundaries, chaotic scattering and they mediate interior crises. >>

<< In this work (AA) discuss the creation of chaotic saddles in a dissipative nontwist system and the interior crises they generate. (They) show how the presence of two saddles increase the transient times and analyze the phenomenon of crisis induced intermittency. >>️

Rodrigo Simile Baroni, Ricardo Egydio de Carvalho, et al. Chaotic saddles and interior crises in a dissipative nontwist system. arXiv: 2211.06921v1 [nlin.CD]. Nov 13, 2022. 

Also

keyword 'intermittency' in FonT

keyword 'dissipation' in FonT

keyword 'saddle' in FonT

keyword 'chaos' | 'chaotic' in Font



keyword 'caos' | 'caotico' in Notes (quasi-stochastic poetry)



Keywords: gst, transitions, dissipation, 
dissipative systems, chaos, saddle, chaotic saddle, crisis, interior crisis, intermittency



lunedì 3 gennaio 2022

# gst: weird but not so weird dynamics, basins with tentacles could be common in high-dimensional systems.


<< Basins of attraction are fundamental to the analysis of dynamical systems (..). Over the years, many remarkable properties of basins have been discovered (..), most notably that their geometry can be wild, as exemplified by Wada basins (..), fractal basin boundaries (..), and riddled or intermingled basins (..). Yet despite these foundational studies, much remains to be learned about basins, especially in systems with many degrees of freedom. >>

AA show that for locally-coupled Kuramoto oscillators << high-dimensional basins tend to have convoluted geometries and cannot be approximated by simple shapes such as hypercubes. Although they are impossible to visualize precisely (because of their high dimensionality), (they) present evidence that these basins have long tentacles that reach far and wide and become tangled with each other. Yet sufficiently close to its own attractor, each basin becomes rounder and more simply structured, somewhat like the head of an octopus. >>

<< In terms of (AA) metaphor, almost all of a basin’s volume is in its tentacles, not its head. This finding is not limited to Kuramoto oscillators. (AA) provide a simple geometrical argument showing that, as long as the number of attractors in a system grows subexponentially with system size, the basins are expected to be octopus-like. As further evidence of their genericity, basins of this type were previously found in simulations of jammed sphere packings (..) where they were described as “branched” and “threadlike” away from a central core (..) and accurate methods were developed for computing their volumes (.,). There is also enticing evidence of octopus-like basins in neuronal networks (..), power grids (..), and photonic couplers (..). >>

<< Figure 4 is a further attempt to visualize the structure of high-dimensional basins, now by examining randomly oriented two-dimensional (2D) slices of state space, either far from a twisted state or close to one. (..) Despite the fact that each basin is connected (..)  the basins look fragmented in this 2D slice. >>

 Fig. 4(a): << Perhaps another metaphor than tentacles—a ball of tangled yarn—better captures the essence of the basin structure in this regime, far from any attractor, in which differently colored threads (representing different basins) are interwoven together in an irregular fashion. >>

Fig. 4(b): << The basin structure near an attractor is strikingly different. (..) the basins near an attractor are organized like an onion. >>

Yuanzhao Zhang, Steven H. Strogatz. Basins with tentacles. arXiv: 2106.05709v3 [nlin.AO]. Nov 2, 2021. 



Also

Reshaping Kuramoto model, when a collective dynamics becomes chaotic, with a surprisingly weak coupling. Dec 27, 2021.


Keywords: gst, dynamical systems, high-dimensional systems, Kuramoto oscillator, attractors, basin of attraction 



mercoledì 11 settembre 2019

# gst: apropos to try numerically to discover states with desired response properties in chaotic (i.e. normal - ab.normal) systems, by Hridesh, Deng, Jean-Jacques, Jeremy.

  <<
Systems with many stable configurations abound in nature, both in living and inanimate matter. Their inherent nonlinearity and sensitivity to small perturbations make them challenging to study, particularly in the presence of external driving, which can alter the relative stability of different attractors. Under such circumstances, one may ask whether any clear relationship holds between the specific pattern of external driving and the particular attractor states selected by a driven multistable system. To gain insight into this question, (AA)  numerically study driven disordered mechanical networks of bistable springs which possess a vast number of stable configurations arising from the two stable rest lengths of each spring, thereby capturing the essential physical properties of a broad class of multistable systems.  (AA) find that the attractor states of driven disordered multistable mechanical networks are fine-tuned with respect to the pattern of external forcing to have low work absorption from it. Furthermore,  (AA)  find that these drive-specific attractor states are even more stable than expected for a given level of work absorption.  (AA)  results suggest that the driven exploration of the vast configuration space of these systems is biased towards states with exceptional relationship to the driving environment, and could therefore be used to 'discover' states with desired response properties in systems with a vast landscape of diverse configurations.
  >>

Hridesh Kedia, Deng Pan, et al. Drive-specific adaptation in disordered mechanical networks of bistable springs. arXiv:1908.09332v1 [nlin.AO] Aug 25, 2019.    https://arxiv.org/abs/1908.09332 

Also

keyword "three" in: FonT    https://flashontrack.blogspot.com/search?q=three

keyword "three" in: Notes      https://inkpi.blogspot.com/search?q=three

sabato 13 agosto 2022

# gst: how a synchronization could emerge from chaotic activities

<< Can we find order in chaos? Physicists have shown, for the first time that chaotic systems can synchronize due to stable structures that emerge from chaotic activity. These structures are known as fractals, shapes with patterns which repeat over and over again in different scales of the shape. As chaotic systems are being coupled, the fractal structures of the different systems will start to assimilate with each other, taking the same form, causing the systems to synchronize. >>️

<< If the systems are strongly coupled, the fractal structures of the two systems will eventually become identical, causing complete synchronization between the systems. These findings help us understand how synchronization and self-organization can emerge from systems that didn't have these properties to begin with, like chaotic systems and biological systems. >>️

Topological synchronization of chaotic systems. Bar-Ilan University. Apr 22, 2022. 

<< chaotic synchronization has a specific trait in various systems, from continuous systems and discrete maps to high dimensional systems: synchronization initiates from the sparse areas of the attractor, and it creates what (AA) termed as the ‘zipper effect’, a distinctive pattern in the multifractal structure of the system that reveals the microscopic buildup of the synchronization process. >>️

Lahav, N., Sendina-Nadal, I., et al. Topological synchronization of chaotic systems. Sci Rep 12, 2508. doi: 10.1038/ s41598-022-06262-z. Feb 15, 2022. 

Also

keyword 'self-assembly' in FonT


Keywords: gst, self-assembly, self-organization, fractals, topological synchronization, zipper effect, chaos, chaotic systems