164 research outputs found
First passage time for subdiffusion: The nonextensive entropy approach versus the fractional model
We study the similarities and differences between different models concerning
subdiffusion. More particularly, we calculate first passage time (FPT)
distributions for subdiffusion, derived from Greens' functions of nonlinear
equations obtained from Sharma-Mittal's, Tsallis's and Gauss's nonadditive
entropies. Then we compare these with FPT distributions calculated from a
fractional model using a subdiffusion equation with a fractional time
derivative. All of Greens' functions give us exactly the same standard relation
which characterizes subdiffusion
(), but generally FPT's are not equivalent to one another. We will
show here that the FPT distribution for the fractional model is asymptotically
equal to the Sharma--Mittal model over the long time limit only if in the
latter case one of the three parameters describing Sharma--Mittal entropy
depends on , and satisfies the specific equation derived in this paper,
whereas the other two models mentioned above give different FTPs with the
fractional model. Greens' functions obtained from the Sharma-Mittal and
fractional models - for obtained from this particular equation - are very
similar to each other. We will also discuss the interpretation of subdiffusion
models based on nonadditive entropies and the possibilities of experimental
measurement of subdiffusion models parameters.Comment: 12 pages, 8 figure
Non-Markovian Levy diffusion in nonhomogeneous media
We study the diffusion equation with a position-dependent, power-law
diffusion coefficient. The equation possesses the Riesz-Weyl fractional
operator and includes a memory kernel. It is solved in the diffusion limit of
small wave numbers. Two kernels are considered in detail: the exponential
kernel, for which the problem resolves itself to the telegrapher's equation,
and the power-law one. The resulting distributions have the form of the L\'evy
process for any kernel. The renormalized fractional moment is introduced to
compare different cases with respect to the diffusion properties of the system.Comment: 7 pages, 2 figure
Anomalous diffusion in the dynamics of complex processes
Anomalous diffusion, process in which the mean-squared displacement of system
states is a non-linear function of time, is usually identified in real
stochastic processes by comparing experimental and theoretical displacements at
relatively small time intervals. This paper proposes an interpolation
expression for the identification of anomalous diffusion in complex signals for
the cases when the dynamics of the system under study reaches a steady state
(large time intervals). This interpolation expression uses the chaotic
difference moment (transient structural function) of the second order as an
average characteristic of displacements. A general procedure for identifying
anomalous diffusion and calculating its parameters in real stochastic signals,
which includes the removal of the regular (low-frequency) components from the
source signal and the fitting of the chaotic part of the experimental
difference moment of the second order to the interpolation expression, is
presented. The procedure was applied to the analysis of the dynamics of
magnetoencephalograms, blinking fluorescence of quantum dots, and X-ray
emission from accreting objects. For all three applications, the interpolation
was able to adequately describe the chaotic part of the experimental difference
moment, which implies that anomalous diffusion manifests itself in these
natural signals. The results of this study make it possible to broaden the
range of complex natural processes in which anomalous diffusion can be
identified. The relation between the interpolation expression and a diffusion
model, which is derived in the paper, allows one to simulate the chaotic
processes in the open complex systems with anomalous diffusion.Comment: 47 pages, 15 figures; Submitted to Physical Review
Fractional Calculus and the Future of Science
Newton foresaw the limitations of geometry’s description of planetary behavior and developed fluxions (differentials) as the new language for celestial mechanics and as the way to implement his laws of mechanics. Two hundred years later Mandelbrot introduced the notion of fractals into the scientific lexicon of geometry, dynamics, and statistics and in so doing suggested ways to see beyond the limitations of Newton’s laws. Mandelbrot’s mathematical essays suggest how fractals may lead to the understanding of turbulence, viscoelasticity, and ultimately to end of dominance of the Newton’s macroscopic world view.Fractional Calculus and the Future of Science examines the nexus of these two game-changing contributions to our scientific understanding of the world. It addresses how non-integer differential equations replace Newton’s laws to describe the many guises of complexity, most of which lay beyond Newton’s experience, and many had even eluded Mandelbrot’s powerful intuition. The book’s authors look behind the mathematics and examine what must be true about a phenomenon’s behavior to justify the replacement of an integer-order with a noninteger-order (fractional) derivative. This window into the future of specific science disciplines using the fractional calculus lens suggests how what is seen entails a difference in scientific thinking and understanding
Bayesian Inference for partially observed SDEs Driven by Fractional Brownian Motion
We consider continuous-time diffusion models driven by fractional Brownian
motion. Observations are assumed to possess a non-trivial likelihood given the
latent path. Due to the non-Markovianity and high-dimensionality of the latent
paths, estimating posterior expectations is a computationally challenging
undertaking. We present a reparameterization framework based on the Davies and
Harte method for sampling stationary Gaussian processes and use this framework
to construct a Markov chain Monte Carlo algorithm that allows computationally
efficient Bayesian inference. The Markov chain Monte Carlo algorithm is based
on a version of hybrid Monte Carlo that delivers increased efficiency when
applied on the high-dimensional latent variables arising in this context. We
specify the methodology on a stochastic volatility model allowing for memory in
the volatility increments through a fractional specification. The methodology
is illustrated on simulated data and on the S&P500/VIX time series and is shown
to be effective. Contrary to a long range dependence attribute of such models
often assumed in the literature, with Hurst parameter larger than 1/2, the
posterior distribution favours values smaller than 1/2, pointing towards medium
range dependence
Recent Advances in Single-Particle Tracking: Experiment and Analysis
This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion
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