Effective Mass Approach to Dynamics of Non-Markovian Systems with Short Memory

Abstract

Memory is an inherent feature of the dynamics of physical systems. It typically emerges when the complex structure of the system is simplified during the modeling process, obscuring a part of its underlying behavior. Consequently, the correct description of such a physical model requires knowledge of not only its present, but also its past states, leading to non-Markovian dynamics. The resulting interactions depending on the system history, however, introduce a significant layer of mathematical complexity. For this reason, when the memory time is significantly shorter than the time scales characterizing the dynamics, it is typically neglected at the cost of losing information regarding its role in the system behavior. In this dissertation, I introduce a novel methodology, namely the effective mass approach, which bridges the Markovian and non-Markovian regimes and provides an accessible framework for analyzing systems with short memory. In this method, the non-Markovian system is approximated with its Markovian counterpart, in which the short-memory effects are encapsulated within an effective mass. I then utilize this approach to demonstrate that the impact of short memory on physical dynamics can be remarkably pronounced. In particular, I show that the directed transport of a Brownian particle in an environment exhibiting temporal correlations can be reversed relative to its behavior in a memoryless medium. Moreover, I propose a setup wherein memory serves as a~control parameter for the generation of random information bits. Collectively, the results of this dissertation underscore the critical importance of short-time correlations in microscopic systems.