Coarse-graining nonequilibrium diffusions with Markov chains

TL;DR

This paper develops a method to approximate nonequilibrium diffusion processes with Markov chains, preserving key steady-state features and enabling entropy production analysis from trajectories.

Contribution

It introduces a finite-volume discretization that accurately captures nonequilibrium steady states and analyzes the limitations of Markov models in estimating entropy production.

Findings

Discretization preserves key features of steady states.

Approximate entropy production converges with increasing states.

Markov models underestimate true entropy production but can detect nonequilibrium.

Abstract

We investigate nonequilibrium steady-state dynamics in both continuous- and discrete-state stochastic processes. Our analysis focuses on planar diffusion dynamics and their coarse-grained approximations by discrete-state Markov chains. Using finite-volume approximations, we derive an approximate master equation directly from the underlying diffusion and show that this discretisation preserves key features of the nonequilibrium steady-state. In particular, we show that the entropy production rate of the approximation converges as the number of discrete states goes to the limit. These results are illustrated with analytically solvable diffusions and numerical experiments on nonlinear processes, demonstrating how this approach can be used to explore the dependence of the entropy production rate on model parameters. Finally, we address the problem of inferring discrete-state Markov models from continuous stochastic trajectories. We show that discrete-state models significantly underestimate the true entropy production rate. However, we also show that they can provide tests to determine if a stationary planar diffusion is out of equilibrium. This property is illustrated with both simulated data and empirical trajectories from schooling fish.