Localizing entropy production along non-equilibrium trajectories

Abstract

Entropy production is a universal measure of irreversibility and energy dissipation in physical, chemical, and biological systems operating far from equilibrium. However, quantifying and spatiotemporally localising it in complex processes directly from experimental data remains a major open challenge. Here we address this issue through a data-driven approach that combines the recently developed short-time thermodynamic uncertainty relation based inference scheme with machine learning techniques. Our approach leverages the flexible function representation provided by deep neural networks to achieve accurate reconstruction of high-dimensional, potentially time-dependent dissipative force fields as well as the localization of fluctuating entropy production in both space and time along nonequilibrium trajectories. We demonstrate the versatility of the framework through applications to diverse systems of fundamental interest and experimental significance, where it successfully addresses distinct challenges in localising entropy production.