Information Length Analysis of Linear Autonomous Stochastic Processes

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    Abstract

    When studying the behaviour of complex dynamical systems, a statistical formulation can provide useful insights. In particular, information geometry is a promising tool for this purpose. In this paper, we investigate the information length for n-dimensional linear autonomous stochastic processes, providing a basic theoretical framework that can be applied to a large set of problems in engineering and physics. A specific application is made to a harmonically bound particle system with the natural oscillation frequency ω, subject to a damping γ and a Gaussian white-noise. We explore how the information length depends on ω and γ, elucidating the role of critical damping γ = 2ω in information geometry. Furthermore, in the long time limit, we show that the information length reflects the linear geometry associated with the Gaussian statistics in a linear stochastic process.
    Original languageEnglish
    Article number1265
    Pages (from-to)1-18
    Number of pages18
    JournalEntropy
    Volume22
    Issue number11
    DOIs
    Publication statusPublished - 7 Nov 2020

    Bibliographical note

    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

    Funder

    Leverhulme Trust Research Fellowship (RF-2018-142-9).

    Keywords

    • Entropy
    • Fluctuations
    • Information geometry
    • Information length
    • Non-equilibrium
    • Stochastic processes
    • Time-dependent PDF

    ASJC Scopus subject areas

    • Physics and Astronomy(all)

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