Continuous Time Random Walk

The Continuous Time Random Walk (CTRW) provides a mathematical framework for the study of transport in heterogenous media. It is much more general than usual transport models based on (local, Markovian) Ordinary Differential Equations, and in particular can handle transport in systems without characteristic scales (such as systems in a state of Self-Organised Criticality or SOC).

Motivation

Interestingly, the absence of local characteristic scales means that effective transport coefficients (the diffusivity etc.) become dependent on the system size, as is indeed suggested by experimental scaling laws for plasma confinement.

In the framework of transport in plasmas, it is believed that the presence of trapping regions (such as turbulent eddies, magnetic islands, internal transport barriers) may lead to sub-diffusion, whereas the occurrence of streamers and profile self-regulation (via turbulence) may lead to super-diffusion. The goal of the CTRW approach is to model the effective transport in the presence of these complex phenomena.

Starting point

CTRW theory starts from the single-particle step distribution function (in one dimension)

$\xi (\Delta x,\Delta t;x,t)$

giving the probability that a particle, located at position x at time t, takes a step of size Δx after waiting a time Δt > 0. All particles are assumed to be identical.

The Master Equation

By making some additional assumptions regarding the nature of this single-particle step distribution, it becomes possible to compute the average behaviour of the system in the limit of infinitely many particles, and to deduce an evolution equation for the particle (probability) density. This equation is a Master Equation.

Fractional Differential Equations

For certain specific choices of the single-particle step distribution (even more restricted than the restrictions needed to derive a Master Equation), the resulting evolution equation for the particle density can be written in the form of a Fractional Differential Equation (FDE).