Extreme Diffusion Measures Statistical Fluctuations of the Environment
Jacob Hass, Hindy Drillick, Ivan Corwin, Eric Corwin

TL;DR
This paper investigates the statistical fluctuations of extreme first passage times and locations in many-particle diffusion within a random environment, revealing universal power-laws and a new diffusion coefficient related to environmental fluctuations.
Contribution
It introduces a universal power-law characterization of environmental contributions to extreme diffusion measures and identifies a novel diffusion coefficient linked to environment fluctuations.
Findings
Universal power-laws for variance of extreme first passage times and locations.
The extreme diffusion coefficient equals the ensemble variance of local drift.
Numerical verification across various RWRE models and system sizes.
Abstract
We consider many-particle diffusion in one spatial dimension modeled as Random Walks in a Random Environment (RWRE). A shared short-range space-time random environment determines the jump distributions that drive the motion of the particles. We determine universal power-laws for the environment's contribution to the variance of the extreme first passage time and extreme location. We show that the prefactors rely upon a single extreme diffusion coefficient that is equal to the ensemble variance of the local drift imposed on particles by the random environment. This coefficient should be contrasted with the Einstein diffusion coefficient, which determines the prefactor in the power-law describing the variance of a single diffusing particle and is equal to the jump variance in the ensemble averaged random environment. Thus a measurement of the behavior of extremes in many-particle…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Systems and Time Series Analysis
