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Localizing entropy production along non-equilibrium trajectories

Biswajit Das,Sreekanth K. Manikandan

Published 2025 in Unknown venue

ABSTRACT

An important open problem in nonequilibrium thermodynamics is the quantification and spatiotemporal localisation of entropy production in complex processes from experimental data. 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.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-03-26

  • Fields of study

    Materials Science, Physics, Computer Science

  • Identifiers
    arXiv 2503.20427
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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