Anomalous Diffusion and the Generalized Langevin Equation

TL;DR

This paper investigates how the memory kernel in the Generalized Langevin Equation influences the long-term anomalous diffusion behavior of particles in viscoelastic fluids, providing a theoretical framework for understanding subdiffusive dynamics.

Contribution

The paper characterizes a class of memory kernels for the GLE, analyzes solution regularity, and links the MSD asymptotics to the kernel's tail behavior, advancing theoretical understanding.

Findings

MSD asymptotics depend on the tail behavior of the memory kernel

Established well-defined classes of kernels for the GLE

Analyzed regularity properties of solutions

Abstract

The Generalized Langevin Equation (GLE) is a Stochastic Integro-Differential Equation that is commonly used to describe the velocity of microparticles that move randomly in viscoelastic fluids. Such particles commonly exhibit what is known as anomalous subdiffusion, which is to say that their position Mean-Squared Displacement (MSD) scales sublinearly with time. While it is common in the literature to observe that there is a relationship between the MSD and the memory structure of the GLE, and there exist special cases where explicit solutions exist, this connection has never been fully characterized. Here, we establish a class of memory kernels for which the GLE is well-defined; we investigate the associated regularity properties of solutions; and we prove that large-time asymptotic behavior of the particle MSD is entirely determined by the tail behavior of the GLE's memory kernel.