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Estimating entropy production with odd-parity state variables via machine learning

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Title
Estimating entropy production with odd-parity state variables via machine learning
Author(s)
Dong-Kyum Kim; Lee, Sangyun; Jeong, Hawoong
Publication Date
2022-04
Journal
Physical Review Research, v.4, no.2
Publisher
American Physical Society
Abstract
© 2022 authors. Published by the American Physical Society.Entropy production (EP) is a central measure in nonequilibrium thermodynamics, as it can quantify the irreversibility of a process as well as its energy dissipation in special cases. Using the time-reversal asymmetry in a system's path probability distribution, many methods have been developed to estimate EP from only trajectory data. However, for systems with odd-parity variables that prevail in nonequilibrium systems, EP estimation via machine learning has not been covered. In this study, we develop a machine-learning method for estimating the EP in a stochastic system with odd-parity variables through multiple neural networks, which enables us to measure EP with only trajectory data and parity information. We demonstrate our method with two systems, an underdamped bead-spring model and a one-particle odd-parity Markov jump process.
URI
https://pr.ibs.re.kr/handle/8788114/12931
DOI
10.1103/PhysRevResearch.4.023051
ISSN
2643-1564
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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