Subordinated diffusion and CTRW asymptotics

TL;DR

This paper compares two numerical methods for modeling anomalous diffusion: Monte Carlo CTRW with Lévy jumps and a subordinated Langevin approach, analyzing their accuracy and computational efficiency against analytical solutions.

Contribution

It provides a detailed comparison of CTRW and subordinated Langevin methods for fractional diffusion, highlighting their respective advantages and trade-offs.

Findings

Subordinated Langevin approach yields higher accuracy.

CTRW with Mittag-Leffler waiting times is computationally efficient.

Both methods approximate the fractional diffusion Green function effectively.

Abstract

Anomalous transport is usually described either by models of continuous time random walks (CTRW) or, otherwise by fractional Fokker-Planck equations (FFPE). The asymptotic relation between properly scaled CTRW and fractional diffusion process has been worked out via various approaches widely discussed in literature. Here, we focus on a correspondence between CTRWs and time and space fractional diffusion equation stemming from two different methods aimed to accurately approximate anomalous diffusion processes. One of them is the Monte Carlo simulation of uncoupled CTRW with a L\'evy $\alpha$-stable distribution of jumps in space and a one-parameter Mittag-Leffler distribution of waiting times. The other is based on a discretized form of a subordinated Langevin equation in which the physical time defined via the number of subsequent steps of motion is itself a random variable. Both approaches are tested for their numerical performance and verified with known analytical solutions for the Green function of a space-time fractional diffusion equation. The comparison demonstrates trade off between precision of constructed solutions and computational costs. The method based on the subordinated Langevin equation leads to a higher accuracy of results, while the CTRW framework with a Mittag-Leffler distribution of waiting times provides efficiently an approximate fundamental solution to the FFPE and converges to the probability density function of the subordinated process in a long-time limit.