Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userStochastic Dynamics of Time Correlations in Complex Systems with Discrete Time
82
0
0.0
(
0
)
Authors
Renat Yulmetyev
Ask ChatGPT about the research
No Arabic abstract
In this paper we present the concept of description of random processes in complex systems with the discrete time. It involves the description of kinetics of discrete processes by means of the chain of finite-difference non-Markov equations for time correlation functions (TCF). We have introduced the dynamic (time dependent) information Shannon entropy $S_i(t)$ where i=0,1,2,3,... as an information measure of stochastic dynamics of time correlation $(i=0)$ and time memory (i=1,2,3,...). The set of functions $S_i(t)$ constitute the quantitative measure of time correlation disorder $(i=0)$ and time memory disorder (i=1,2,3,...) in complex system. Harnessing the infinite set of orthogonal dynamic random variables on a basis of Gram-Shmidt orthogonalization procedure tends to creation of infinite chain of finite-difference non-Markov kinetic equations for discrete TCF and memory function.The solution of the equations above thereof brings to the recurrence relations between the TCF and MF of senior and junior orders. The results obtained offer considerable scope for attack on stochastic dynamics of discrete random processes in a complex systems. Application of this technique on the analysis of stochastic dynamics of RR-intervals from human ECGs shows convincing evidence for a non-Markovian phenomemena associated with a peculiarities in short and long-range scaling. This method may be of use in distinguishing healthy from pathologic data sets based in differences in these non-Markovian properties.
rate research
Read More
The neuromagnetic activity (magnetoencephalogram, MEG) from healthy human brain and from an epileptic patient against chromatic flickering stimuli has been earlier analyzed on the basis of a memory functions formalism (MFF). Information measures of memory as well as relaxation parameters revealed high individuality and unique features in the neuromagnetic brain responses of each subject. The current paper demonstrates new capabilities of MFF by studying cross-correlations between MEG signals obtained from multiple and distant brain regions. It is shown that the MEG signals of healthy subjects are characterized by well-defined effects of frequency synchronization and at the same time by the domination of low-frequency processes. On the contrary, the MEG of a patient is characterized by a sharp abnormality of frequency synchronization, and also by prevalence of high-frequency quasi-periodic processes. Modification of synchronization effects and dynamics of cross-correlations offer a promising method of detecting pathological abnormalities in brain responses.
We describe a possible general and simple paradigm in a classical thermal setting for discrete time crystals (DTCs), systems with stable dynamics which is subharmonic to the driving frequency thus breaking discrete time-translational invariance. We consider specifically an Ising model in two dimensions, as a prototypical system with a phase transition into stable phases distinguished by a local order parameter, driven by a thermal dynamics and periodically kicked. We show that for a wide parameter range a stable DTC emerges. The phase transition to the DTC state appears to be in the equilibrium 2D Ising class when dynamics is observed stroboscopically. However, we show that the DTC is a genuine non-equilibrium state. More generally, we speculate that systems with thermal phase transitions to multiple competing phases can give rise to DTCs when appropriately driven.
A stochastic dynamics framework for the study of complex systems is presented.
We propose a method to obtain phase portraits for stochastic systems. Starting from the Fokker-Planck equation, we separate the dynamics into a convective and a diffusive part. We show that stable and unstable fixed points of the convective field correspond to maxima and minima of the stationary probability distribution if the probability current vanishes at these points. Stochastic phase portraits, which are vector plots of the convective field, therefore indicate the extrema of the stationary distribution and can be used to identify stochastic bifurcations that change the number and stability of these extrema. We show that limit cycles in stochastic phase portraits can indicate ridges of the probability distribution, and we identify a novel type of stochastic bifurcations, where the probability maximum moves to the edge of the system through a gap between the two nullclines of the convective field.
Continuous time random Walk model has been versatile analytical formalism for studying and modeling diffusion processes in heterogeneous structures, such as disordered or porous media. We are studying the continuous limits of Heterogeneous Continuous Time Random Walk model, when a random walk is making jumps on a graph within different time-length. We apply the concept of a generalized master equation to study heterogeneous continuous-time random walks on networks. Depending on the interpretations of the waiting time distributions the generalized master equation gives different forms of continuous equations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
