Information Geometry Control under the Laplace Assumption

Adrian-Josue Guel-Cortez, Eun-jin Kim

Research output: Contribution to journalConference articlepeer-review

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Abstract

By combining information science and differential geometry, information geometry provides a geometric method to measure the differences in the time evolution of the statistical states in a stochastic process. Specifically, the so-called information length (the time integral of the information rate) describes the total amount of statistical changes that a time-varying probability distribution takes through time. In this work, we outline how the application of information geometry may permit us to create energetically efficient and organised behaviour artificially. Specifically, we demonstrate how nonlinear stochastic systems can be analysed by utilising the Laplace assumption to speed up the numerical computation of the information rate of stochastic dynamics. Then, we explore a modern control engineering protocol to obtain the minimum statistical variability while analysing its effects on the closed-loop system’s stochastic thermodynamics.
Original languageEnglish
Article number25
Number of pages12
JournalPhysical Sciences Forum
Volume5
Issue number1
DOIs
Publication statusPublished - 12 Dec 2022
Event41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Paris, France
Duration: 18 Jul 202222 Jul 2022

Bibliographical note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Keywords

  • information geometry
  • non-linear stochastic systems
  • information length
  • stochastic thermodynamics

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