The diffusion-drift equation on comb-like structure

2007

Starting from the model of continuous time random walk that can also be considered as a compound renewal process we focus our interest on random walks in which the probability distributions of the waiting times and jumps have fat tails characterized by power laws with exponent between 0 and 1 for the waiting times, between 0 and 2 for the jumps. By stating the relevant lemmata (of Tauber type) for the distribution functions we need not distinguish between continuous and discrete space and time. We will see that by a well-scaled passage to the diffusion limit diffusion processes fractional in time as well as in space are obtained. The corresponding equation of evolution is a linear partial pseudo-differential equation with fractional derivatives in time and in space, the orders being equal to the above exponents. Such processes are enjoying increasing popularity in applications in physics, chemistry, finance and other fields, and their behaviour can be well approximated and visualized by simulation via various types of random walks. For their explicit solutions there are available integral representations that allow to investigate their detailed structure. For ease of presentation we restrict attention to the spatially one-dimensional symmetric situation.

The comb model is a simplified description for anomalous diffusion under geometric constraints. It represents particles spreading out in a two-dimensional space where the motions in the x-direction are allowed only when the y coordinate of the particle is zero. Here, we propose an extension for the comb model via Langevin-like equations driven by fractional Gaussian noises (longrange correlated). By carrying out computer simulations, we show that the correlations in the y-direction affect the diffusive behavior in the x-direction in a non-trivial fashion, resulting in a quite rich diffusive scenario characterized by usual, superdiffusive or subdiffusive scaling of second moment in the x-direction. We further show that the long-range correlations affect the probability distribution of the particle positions in the x-direction, making their tails longer when noise in the y-direction is persistent and shorter for anti-persistent noise. Our model thus combines and allows the study/analysis of the interplay between Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Journal of Statistical Physics

The temporal Fokker-Planck equation (Boon et al. in J Stat Phys 3/4: 527, 2003) or propagation-dispersion equation was derived to describe diffusive processes with temporal dispersion rather than spatial dispersion as in classical diffusion. We present two generalizations of the temporal Fokker-Planck equation for the first passage distribution function f j (r, t) of a particle moving on a substrate with time delays τ j. Both generalizations follow from the first visit recurrence relation. In the first case, the time delays depend on the local concentration, that is the time delay probability P j is a functional of the particle distribution function and we show that when the functional dependence is of the power law type, P j ∝ f ν−1 j , the generalized Fokker-Planck equation exhibits a structure similar to that of the nonlinear spatial diffusion equation where the roles of space and time are reversed. In the second case, we consider the situation where the time delays are distributed according to a power law, P j ∝ τ −1−α j (with 0 < α < 2), in which case we obtain a fractional propagationdispersion equation which is the temporal analog of the fractional spatial diffusion equation (with space and time interchanged). The analysis shows how certain microscopic mechanisms can lead to non-Gaussian distributions and non-classical scaling exponents.

Physical Review E, 1998

We analyze a simple model of deterministic diffusion. The model consists of a one-dimensional periodic array of scatterers in which point particles move from cell to cell as defined by a piecewise linear map. The microscopic chaotic scattering process of the map can be changed by a control parameter. This induces a parameter dependence for the macroscopic diffusion coefficient. We calculate the diffusion coefficent and the largest eigenmodes of the system by using Markov partitions and by solving the eigenvalue problems of respective topological transition matrices. For different boundary conditions we find that the largest eigenmodes of the map match to the ones of the simple phenomenological diffusion equation. Our main result is that the difffusion coefficient exhibits a fractal structure by varying the system parameter. To understand the origin of this fractal structure, we give qualitative and quantitative arguments. These arguments relate the sequence of oscillations in the strength of the parameter-dependent diffusion coefficient to the microscopic coupling of the single scatterers which changes by varying the control parameter.

Lecture Notes in Physics, 2003

A physical-mathematical approach to anomalous diffusion may be based on generalized diffusion equations (containing derivatives of fractional order in space or/and time) and related random walk models. By the spacetime fractional diffusion equation we mean an evolution equation obtained from the standard linear diffusion equation by replacing the second-order space derivative with a Riesz-Feller derivative of order α ∈ (0, 2] and skewness θ (|θ| ≤ min {α, 2 − α}), and the first-order time derivative with a Caputo derivative of order β ∈ (0, 1] . The fundamental solution (for the Cauchy problem) of the fractional diffusion equation can be interpreted as a probability density evolving in time of a peculiar self-similar stochastic process. We view it as a generalized diffusion process that we call fractional diffusion process, and present an integral representation of the fundamental solution. A more general approach to anomalous diffusion is however known to be provided by the master equation for a continuous time random walk (CTRW). We show how this equation reduces to our fractional diffusion equation by a properly scaled passage to the limit of compressed waiting times and jump widths. Finally, we describe a method of simulation and display (via graphics) results of a few numerical case studies.

Chaos, Solitons &amp; Fractals, 2013

A Fokker-Planck equation on fractal curves is obtained, starting from Chapmann-Kolmogorov equation on fractal curves. This is done using the recently developed calculus on fractals, which allows one to write differential equations on fractal curves. As an important special case, the diffusion and drift coefficients are obtained, for a suitable transition probability to get the diffusion equation on fractal curves. This equation is of first order in time, and, in space variable it involves derivatives of order α, α being the dimension of the curve. The solution of this equation with localized initial condition shows deviation from ordinary diffusion behaviour due to underlying fractal space in which diffusion is taking place. An exact solution of this equation manifests a subdiffusive behaviour. The dimension of the fractal path can be estimated from the distribution function.

We build a family of tree-like fractals parametrised by a real number 0<a<1. After constructing a diffusion process on these fractals, we tackle the problem of constructing diffusions on a tree-like fractal whose parameter a(t) is time-dependent. We also study the near-constancy behaviour on these fractals, in order to see how it is affected by the parameter a.

2007

A physical-mathematical approach to anomalous diffusion may be based on generalized diffusion equations (containing derivatives of fractional order in space or/and time) and related random walk models. By the spacetime fractional diffusion equation we mean an evolution equation obtained from the standard linear diffusion equation by replacing the second-order space derivative with a Riesz-Feller derivative of order α ∈ (0, 2] and skewness θ (|θ| ≤ min {α, 2 − α}), and the first-order time derivative with a Caputo derivative of order β ∈ (0, 1] . The fundamental solution (for the Cauchy problem) of the fractional diffusion equation can be interpreted as a probability density evolving in time of a peculiar self-similar stochastic process. We view it as a generalized diffusion process that we call fractional diffusion process, and present an integral representation of the fundamental solution. A more general approach to anomalous diffusion is however known to be provided by the master equation for a continuous time random walk (CTRW). We show how this equation reduces to our fractional diffusion equation by a properly scaled passage to the limit of compressed waiting times and jump widths. Finally, we describe a method of simulation and display (via graphics) results of a few numerical case studies.

Physics Reports, 2000

What can fractional equations do, what can they do better, and why should one care at all? 1 1.2. What is the scope of this report? 5 2. Introduction 5 2.1. Anomalous dynamics in complex systems 5 2.2. Historical remarks 7 2.3. Anomalous di!usion: experiments and models 9 3. From continuous time random walk to fractional di!usion equations 13 3.1. Revisiting the realm of Brownian motion 14 3.2. The continuous time random walk model 15 3.3. Back to Brownian motion 17 3.4. Long rests: a fractional di!usion equation describing subdi!usion 18 3.5. Long jumps: LeH vy #ights 25 3.6. The competition between long rests and long jumps 29 3.7. What's the course, helmsman? 30 4. Fractional di!usion}advection equations 31 4.1. The Galilei invariant fractional di!usion}advection equation 32 4.2. The Gallilei variant fractional di!usion}advection equation 4.3. Alternative approaches for LeH vy #ights 5. The fractional Fokker}Planck equation: anomalous di!usion in an external force "eld 5.1. The Fokker}Planck equation 5.2. The fractional Fokker}Planck equation 5.3. Separation of variables and the fractional Ornstein}Uhlenbeck process 5.4. The connection between the fractional solution and its Brownian counterpart 5.5. The fractional analogue of Kramers escape theory from a potential well 5.6. The derivation of the fractional Fokker}Planck equation 5.7. A fractional Fokker}Planck equation for LeH vy #ights 5.8. A generalised Kramers}Moyal expansion 6. From the Langevin equation to fractional di!usion: microscopic foundation of dispersive transport close to thermal equilibrium 6.1. Langevin dynamics and the three stages to subdi!usion

Arxiv preprint cond-mat/0001081, 2000

Physica A: Statistical Mechanics and its Applications, 1992

A differential equation for diffusion in isotropic and homogeneous ffactal structures is derived within the context of fractional calculus. It generalizes the fractional diffusion equation valid in Euclidean systems. The asymptotic behavior of the probability density function is obtained exactly and coincides with the accepted asymptotic form obtained using scaling argument and exact enumeration calculations on large percolation clusters at criticality. The asymptotic frequency dependence of the scattering function is derived exactly from the present approach, which can be studied by X-ray and neutron scattering experiments on fractals.

Physics Letters A, 2003

We present a fractional diffusion equation involving external force fields for transport phenomena in random media. It is shown that this fractional diffusion equation obey generalized Einstein relation, and its stationary solution is the Boltzmann distribution. It is proved that the asymptotic behavior of its solution is stretched Gaussian and that its solution can be expressed in the form of a function of a dimensionless similarity variable, not only for constant potentials but also for logarithm, harmonic, analytic and generic potentials. A comparison with the fractional Fokker-Planck equation is given.

SIAM Journal on Applied Mathematics, 2015

Continuous time random walks, which generalize random walks by adding a stochastic time between jumps, provide a useful description of stochastic transport at mesoscopic scales. The continuous time random walk model can accommodate certain features, such as trapping, which are not manifest in the standard macroscopic diffusion equation. The trapping is incorporated through a waiting time density, and a fractional diffusion equation results from a power law waiting time. A generalized continuous time random walk model with biased jumps has been used to consider transport that is also subject to an external force. Here we have derived the master equations for continuous time random walks with space-and time-dependent forcing for two cases: when the force is evaluated at the start of the waiting time and at the end of the waiting time. The differences persist in low order spatial continuum approximations; however, the two processes are shown to be governed by the same Fokker-Planck equations in the diffusion limit. Thus the fractional Fokker-Planck equation with space-and time-dependent forcing is robust to these changes in the underlying stochastic process.

Frontiers in Physics

The generalized diffusion equations with fractional order derivatives have shown be quite efficient to describe the diffusion in complex systems, with the advantage of producing exact expressions for the underlying diffusive properties. Recently, researchers have proposed different fractionaltime operators (namely: the Caputo-Fabrizio and Atangana-Baleanu) which, differently from the well-known Riemann-Liouville operator, are defined by non-singular memory kernels. Here we proposed to use these new operators to generalize the usual diffusion equation. By analyzing the corresponding fractional diffusion equations within the continuous time random walk framework, we obtained waiting time distributions characterized by exponential, stretched exponential, and powerlaw functions, as well as a crossover between two behaviors. For the mean square displacement, we found crossovers between usual and confined diffusion, and between usual and sub-diffusion. We obtained the exact expressions for the probability distributions, where non-Gaussian and stationary distributions emerged. This former feature is remarkable because the fractional diffusion equation is solved without external forces and subjected to the free diffusion boundary conditions. We have further shown that these new fractional diffusion equations are related to diffusive processes with stochastic resetting, and to fractional diffusion equations with derivatives of distributed order. Thus, our results show that these new operators are a simple and efficient way for incorporating different structural aspects into the system, opening new possibilities for modeling and investigating anomalous diffusive processes.

Statistics & Probability Letters, 2013

Continuous time random walks impose a random waiting time before each particle jump. Scaling limits of heavy-tailed continuous time random walks are governed by fractional evolution equations. Space-fractional derivatives describe heavy-tailed jumps, and the time-fractional version codes heavy-tailed waiting times. This paper develops scaling limits and governing equations in the case of correlated jumps. For long-range dependent jumps, this leads to fractional Brownian motion or linear fractional stable motion, with the time parameter replaced by an inverse stable subordinator in the case of heavy-tailed waiting times. These scaling limits provide an interesting class of non-Markovian, non-Gaussian self-similar processes.