Universal Response Inequalities Beyond Steady States via Trajectory Information Geometry

TL;DR

This paper develops a trajectory information geometric framework to understand and bound the response of non-stationary, far-from-equilibrium Markov processes, extending response theory beyond steady states.

Contribution

It introduces a complete geometric approach that generalizes response theory for non-stationary systems, deriving universal inequalities and linking system sensitivity with geometric structure.

Findings

Derives a diagonal Fisher information metric for non-stationary Markov processes.

Establishes a universal nonlinear response inequality based on geodesic length.

Connects dynamical activity, observable variance, and system sensitivity through geometry.

Abstract

Fluctuation-dissipation relations elucidate the response of near-equilibrium systems to environmental changes, with recent advances extending response theory to non-equilibrium steady states. However, a general response theory for systems evolving far from steady states has remained elusive. This letter presents a complete trajectory information geometric framework that generalizes response theory for non-stationary Markov processes. By constructing the full trajectory probability manifold and identifying a globally orthogonal coordinate system defined by transition rates, we derive a diagonal Fisher information metric that enables explicit calculations in this high-dimensional space. From the local metric structure, we obtain a Cramer-Rao-type inequality that bounds the linear response of arbitrary non-stationary observables. Furthermore, by analyzing the global geometry of this manifold, we derive a universal non-perturbative (nonlinear) response inequality in terms of geodesic length. This geometric framework reveals deep connections between dynamical activity, observable variance, and system sensitivity, and it encompasses or anticipates several recent results as special cases. Our approach offers new design principles for responsive behaviors in far-from-equilibrium systems.