Asymmetric relaxations through the lens of information geometry

Abstract We frame Newton’s law of cooling as a gradient flow within the context of information geometry. This connects it to a thermodynamic uncertainty relation and the Horse–Carrot theorem, and reveals novel instances of asymmetric relaxations in endoreversible processes. We present a general criterion for predicting asymmetries using the Amari–Chentsov tensor, applicable to classical and quantum thermodynamics. Examples include faster cooling of quantum ideal gases and relaxations that resemble the Mpemba effect in classical ideal gases.

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Publication Details

Journal
Journal of Physics A Mathematical and Theoretical
Published
2025-03-14
DOI
https://doi.org/10.1088/1751-8121/adc0d0
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
7.44

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article

Asymmetric relaxations through the lens of information geometry

Pablo Padilla, Alessandro Bravetti, Miguel Ángel García-Ariza
3 citations
Journal of Physics A Mathematical and Theoretical
Neural Networks and Applications
7.44
article

Asymmetric relaxations through the lens of information geometry

Pablo Padilla, Alessandro Bravetti, Miguel Ángel García-Ariza
article en
3 citations

Abstract

Abstract We frame Newton’s law of cooling as a gradient flow within the context of information geometry. This connects it to a thermodynamic uncertainty relation and the Horse–Carrot theorem, and reveals novel instances of asymmetric relaxations in endoreversible processes. We present a general criterion for predicting asymmetries using the Amari–Chentsov tensor, applicable to classical and quantum thermodynamics. Examples include faster cooling of quantum ideal gases and relaxations that resemble the Mpemba effect in classical ideal gases.

Journal of Physics A Mathematical and TheoreticalVol. 58(12)
Università di Camerino (IT), Universidad Nacional Autónoma de México (MX)
Consejo Nacional de Ciencia y Tecnología, Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
Openalex Percentile: Top 4%
Neural Networks and Applications
7.44
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