2024/03/16 by Jiming Zheng, Zheng, Jiming, Zhiyue Lu +1 · 3 citations
Computer Science · #Biological Physics (physics.bio-ph) #FOS: Physical sciences #Neural Networks and Applications #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.2403.10952
openalex publication_date 2024/03/16 · openalex created_date 2024/03/21 · openalex updated_date 2026/07/28
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.