4.3 Article

Large Deviations and Gradient Flows for the Brownian One-Dimensional Hard-Rod System

Journal

POTENTIAL ANALYSIS
Volume 58, Issue 1, Pages 71-121

Publisher

SPRINGER
DOI: 10.1007/s11118-021-09933-0

Keywords

Steric interaction; Volume exclusion; Hard-sphere; Hard-rod; Large deviations; Continuum limit; Brownian motion

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In this study, we investigate a one-dimensional system of hard rods and prove a large-deviation principle for the empirical measure of the rod positions. We find that the limit evolution of the system exhibits a gradient-flow structure with an entropy-like driving functional and Wasserstein dissipation. This research has important implications for the modeling of interacting particle systems with finite-size effects.
We study a system of hard rods of finite size in one space dimension, which move by Brownian noise while avoiding overlap. We consider a scaling in which the number of particles tends to infinity while the volume fraction of the rods remains constant; in this limit the empirical measure of the rod positions converges almost surely to a deterministic limit evolution. We prove a large-deviation principle on path space for the empirical measure, by exploiting a one-to-one mapping between the hard-rod system and a system of non-interacting particles on a contracted domain. The large-deviation principle naturally identifies a gradient-flow structure for the limit evolution, with clear interpretations for both the driving functional (an 'entropy') and the dissipation, which in this case is the Wasserstein dissipation. This study is inspired by recent developments in the continuum modelling of multiple-species interacting particle systems with finite-size effects; for such systems many different modelling choices appear in the literature, raising the question how one can understand such choices in terms of more microscopic models. The results of this paper give a clear answer to this question, albeit for the simpler one-dimensional hard-rod system. For this specific system this result provides a clear understanding of the value and interpretation of different modelling choices, while giving hints for more general systems.

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