Current fluctuations for the boundary-driven zero-range process on graphs: microscopic versus macroscopic approach and a theory of non-reversible resistor-like networks

TL;DR

This paper derives a large deviation rate functional for boundary-driven zero-range processes on graphs, connecting microscopic and macroscopic perspectives, and introduces a reduction method related to non-reversible electrical networks.

Contribution

It provides a novel variational derivation of the rate functional for current fluctuations on graphs, extending previous one-dimensional results and linking to non-reversible network theory.

Findings

The rate functional converges to the macroscopic fluctuation theory prediction.

Effective edges can be derived using electrical network analogies.

The reduction method applies to non-reversible networks and captures current fluctuations.

Abstract

We compute the joint large deviation rate functional in the limit of large time for the current flowing through the edges of a finite graph on which a boundary-driven system of stochastic particles evolves with zero-range dynamics.This generalizes one-dimensional results previously obtained with different approaches; our alternative techniques illuminate various connections and complementary perspectives. In particular, we here use a variational approach to derive the rate functional by contraction from a level 2.5 large deviation rate functional. We perform an exact minimization and finally obtain the rate functional as a variational problem involving a superposition of cost functions for each edge. The contributions from different edges are not independent since they are related by the values of a potential function on the nodes of the graph. The rate functional on the graph is a microscopic version of the continuous rate functional predicted by the macroscopic fluctuation theory, and we indeed show a convergence in the scaling limit. If we split the graph into two connected regions by a cutset and are interested just in the current flowing through the cutset, we find that the result is the same as that of an effective system composed of only one effective edge (as happens at macroscopic level and is expected also for other models). The characteristics of this effective edge are related to the ``capacities'' of the graph and can be obtained by a reduction using elementary transformations as in electrical networks; specifically, we treat components in parallel, in series, and in $N$-star configurations (reduced to effective complete $N$-graphs). Our reduction procedure is directly related to the reduction to the trace process and, since the dynamics is in general not reversible, it is also closely connected to the theory of non-reversible electrical networks.