A Semi-Markov Algorithm for Continuous Time Random Walk Limit Distributions

TL;DR

This paper develops a semi-Markov algorithm to compute distributions of continuous time random walk (CTRW) limit processes, enabling numerical approximation of their dynamics and initial conditions, with applications to various anomalous diffusion models.

Contribution

It introduces a semi-Markov framework and numerical method for CTRW limit distributions, accommodating arbitrary initial ages and diverse temporal scalings.

Findings

Successfully computes distributions for CTRW limits.

Demonstrates the method on subdiffusion and fractal models.

Provides a flexible approach for initial age conditions.

Abstract

The Semi-Markov property of Continuous Time Random Walks (CTRWs) and their limit processes is utilized, and the probability distributions of the bivariate Markov process $(X(t),V(t))$ are calculated: $X(t)$ is a CTRW limit and $V(t)$ a process tracking the age, i.e. the time since the last jump. For a given CTRW limit process $X(t)$, a sequence of discrete CTRWs in discrete time is given which converges to $X(t)$ (weakly in the Skorokhod topology). Master equations for the discrete CTRWs are implemented numerically, thus approximating the distribution of $X(t)$. A consequence of the derived algorithm is that any distribution of initial age can be assumed as an initial condition for the CTRW limit dynamics. Four examples with different temporal scaling are discussed: subdiffusion, tempered subdiffusion, the fractal mobile/immobile model and the tempered fractal mobile/immobile model.