Fermionic theory of nonequilibrium steady states

Fuente: arXiv
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Autori principali: De Giuli, Eric, Shimada, Masanari
Natura: Preprint
Pubblicazione: 2023
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author De Giuli, Eric
Shimada, Masanari
author_facet De Giuli, Eric
Shimada, Masanari
contents As the quantification of metabolism, nonequilibrium steady states play a central role in living matter, but are beyond the purview of equilibrium statistical mechanics. Here we develop a fermionic theory of nonequilibrium steady states in continuous-time Markovian systems, generalizing Boltzmann-Gibbs statistical mechanics to this case. The response to an arbitrary perturbation is computed, and simplified in canonical cases. Beyond response, we consider ensembles of nonequilibrium steady states and show that a general class of ensembles is described by a 2D statistical field theory with infinitesimally broken supersymmetry, which may form the basis of nontrivial solvable models of nonequilibrium steady states.
format Preprint
id arxiv_https___arxiv_org_abs_2308_10744
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Fermionic theory of nonequilibrium steady states
De Giuli, Eric
Shimada, Masanari
Statistical Mechanics
Disordered Systems and Neural Networks
As the quantification of metabolism, nonequilibrium steady states play a central role in living matter, but are beyond the purview of equilibrium statistical mechanics. Here we develop a fermionic theory of nonequilibrium steady states in continuous-time Markovian systems, generalizing Boltzmann-Gibbs statistical mechanics to this case. The response to an arbitrary perturbation is computed, and simplified in canonical cases. Beyond response, we consider ensembles of nonequilibrium steady states and show that a general class of ensembles is described by a 2D statistical field theory with infinitesimally broken supersymmetry, which may form the basis of nontrivial solvable models of nonequilibrium steady states.
title Fermionic theory of nonequilibrium steady states
topic Statistical Mechanics
Disordered Systems and Neural Networks
url https://arxiv.org/abs/2308.10744