scholarly journals Introduction to focus issue: In memory of Vadim S. Anishchenko: Statistical physics and nonlinear dynamics of complex systems

2022 ◽  
Vol 32 (1) ◽  
pp. 010401
Author(s):  
Anna Zakharova ◽  
Galina Strelkova ◽  
Eckehard Schöll ◽  
Jürgen Kurths
2009 ◽  
Vol 17 (2) ◽  
pp. 237-248 ◽  
Author(s):  
Gregoire Nicolis ◽  
Catherine Nicolis

An approach to Complexity from the perspective of fundamental science is outlined, drawing on the cross-fertilization of concepts and tools from nonlinear dynamics, statistical physics, probability and information theories, data analysis and numerical simulation. Emphasis is placed on the intertwining between different levels of description and on the probabilistic dimension of complex systems, in connection with the issue of prediction.


2012 ◽  
Vol 23 (13) ◽  
pp. 2403-2406 ◽  
Author(s):  
Eric Karsenti

In this essay I describe my personal journey from reductionist to systems cell biology and describe how this in turn led to a 3-year sea voyage to explore complex ocean communities. In describing this journey, I hope to convey some important principles that I gleaned along the way. I realized that cellular functions emerge from multiple molecular interactions and that new approaches borrowed from statistical physics are required to understand the emergence of such complex systems. Then I wondered how such interaction networks developed during evolution. Because life first evolved in the oceans, it became a natural thing to start looking at the small organisms that compose the plankton in the world's oceans, of which 98% are … individual cells—hence the Tara Oceans voyage, which finished on 31 March 2012 in Lorient, France, after a 60,000-mile around-the-world journey that collected more than 30,000 samples from 153 sampling stations.


Author(s):  
Marisa Faggini ◽  
Bruna Bruno ◽  
Anna Parziale

AbstractFollowing the reverse engineering (RE) approach to analyse an economic complex system is to infer how its underlying mechanism works. The main factors that condition the difficulty of RE are the number of variable components in the system and, most importantly, the interdependence of components on one another and nonlinear dynamics. All those aspects characterize the economic complex systems within which economic agents make their choices. Economic complex systems are adopted in RE science, and they could be used to understand, predict and model the dynamics of the complex systems that enable to define and to control the economic environment. With the RE approach, economic data could be used to peek into the internal workings of the economic complex system, providing information about its underling nonlinear dynamics. The idea of this paper arises from the aim to deepen the comprehension of this approach and to highlight the potential implementation of tools and methodologies based on it to treat economic complex systems. An overview of the literature about the RE is presented, by focusing on the definition and on the state of the art of the research, and then we consider two potential tools that could translate the methodological issues of RE by evidencing advantages and disadvantages for economic analysis: the recurrence analysis and the agent-based model (ABM).


2006 ◽  
Vol 16 (07) ◽  
pp. 1889-1911 ◽  
Author(s):  
PETER A. TASS ◽  
CHRISTIAN HAUPTMANN ◽  
OLEKSANDR V. POPOVYCH

Synchronization processes may severely impair brain function, for instance, in Parkinson's disease, essential tremor or epilepsies. We present three different effectively desynchronizing stimulation techniques which have been developed with methods from nonlinear dynamics and statistical physics. These techniques exploit either stochastic phase resetting principles or complex delayed feedback mechanisms. We explain how these methods work and how they can be applied to therapeutic brain stimulation.


2020 ◽  
Vol 30 (6) ◽  
pp. 063151 ◽  
Author(s):  
Yang Tang ◽  
Jürgen Kurths ◽  
Wei Lin ◽  
Edward Ott ◽  
Ljupco Kocarev

2000 ◽  
Vol 160 (1) ◽  
pp. 199-206
Author(s):  
P. Kurakin ◽  
G. Malinetsky ◽  
A. Podlazov

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