Memory corrections to Markovian Langevin dynamics

TL;DR

This paper develops a Markovian embedding approach to approximate non-Markovian Langevin dynamics with memory corrections, enabling more accurate modeling of correlated thermal fluctuations in Brownian systems.

Contribution

It introduces a method to derive higher order memory corrections to Markovian Langevin dynamics using the embedding technique and Prony series representation.

Findings

First and second order memory corrections calculated

Second order correction further reduces effective mass

Method improves approximation accuracy for non-Markovian systems

Abstract

Analysis of non-Markovian systems and memory induced phenomena poses an everlasting challenge for physics. As a paradigmatic example we consider a classical Brownian particle of mass $M$ subjected to an external force and exposed to correlated thermal fluctuations. We show that the recently developed approach to this system, in which its non-Markovian dynamics given by the Generalized Langevin Equation is approximated by its memoryless counterpart but with the effective particle mass $M^* < M$, can be derived within the Markovian embedding technique. Using this method we calculate the first and the second order memory correction to Markovian dynamics of the Brownian particle for the memory kernel represented as the Prony series. The second one lowers the effective mass of the system further and improves precision of the approximation. Our work opens the door for the derivation of higher order memory corrections to Markovian Langevin dynamics.