{"title":"索引系综的排列熵:量化热化动力学","authors":"A. Aragoneses, A. Kapulkin, Arjendu K. Pattanayak","doi":"10.1088/2632-072X/acd742","DOIUrl":null,"url":null,"abstract":"We introduce ‘PI-Entropy’ Π(ρ˜) (the Permutation entropy of an Indexed ensemble) to quantify mixing due to complex dynamics for an ensemble ρ of different initial states evolving under identical dynamics. We find that Π(ρ˜) acts as an excellent proxy for the thermodynamic entropy S(ρ) but is much more computationally efficient. We study 1-D and 2D iterative maps and find that Π(ρ˜) dynamics distinguish a variety of system time scales and track global loss of information as the ensemble relaxes to equilibrium. There is a universal S-shaped relaxation to equilibrium for generally chaotic systems, and this relaxation is characterized by a shuffling timescale that correlates with the system’s Lyapunov exponent. For the Chirikov Standard Map, a system with a mixed phase space where the chaos grows with nonlinear kick strength K, we find that for high K, Π(ρ˜) behaves like the uniformly hyperbolic 2D Cat Map. For low K we see periodic behavior with a relaxation envelope resembling those of the chaotic regime, but with frequencies that depend on the size and location of the initial ensemble in the mixed phase space as well as K. We discuss how Π(ρ˜) adapts to experimental work and its general utility in quantifying how complex systems change from a low entropy to a high entropy state.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Permutation entropy of indexed ensembles: quantifying thermalization dynamics\",\"authors\":\"A. Aragoneses, A. Kapulkin, Arjendu K. Pattanayak\",\"doi\":\"10.1088/2632-072X/acd742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce ‘PI-Entropy’ Π(ρ˜) (the Permutation entropy of an Indexed ensemble) to quantify mixing due to complex dynamics for an ensemble ρ of different initial states evolving under identical dynamics. We find that Π(ρ˜) acts as an excellent proxy for the thermodynamic entropy S(ρ) but is much more computationally efficient. We study 1-D and 2D iterative maps and find that Π(ρ˜) dynamics distinguish a variety of system time scales and track global loss of information as the ensemble relaxes to equilibrium. There is a universal S-shaped relaxation to equilibrium for generally chaotic systems, and this relaxation is characterized by a shuffling timescale that correlates with the system’s Lyapunov exponent. For the Chirikov Standard Map, a system with a mixed phase space where the chaos grows with nonlinear kick strength K, we find that for high K, Π(ρ˜) behaves like the uniformly hyperbolic 2D Cat Map. For low K we see periodic behavior with a relaxation envelope resembling those of the chaotic regime, but with frequencies that depend on the size and location of the initial ensemble in the mixed phase space as well as K. We discuss how Π(ρ˜) adapts to experimental work and its general utility in quantifying how complex systems change from a low entropy to a high entropy state.\",\"PeriodicalId\":53211,\"journal\":{\"name\":\"Journal of Physics Complexity\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2022-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Physics Complexity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2632-072X/acd742\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics Complexity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2632-072X/acd742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
我们引入' pi -熵' Π(ρ≈)(索引系综的排列熵)来量化由于在相同动力学下演化的不同初始状态的系综ρ的复杂动力学而引起的混合。我们发现Π(ρ≈)作为热力学熵S(ρ)的极好代理,但计算效率更高。我们研究了1-D和2D迭代映射,发现Π(ρ≈)动力学区分了各种系统时间尺度,并在集成松弛到平衡时跟踪全局信息损失。对于一般混沌系统来说,存在一个普遍的s形松弛到平衡,这种松弛的特征是与系统的李雅普诺夫指数相关的洗牌时间尺度。对于具有混合相空间的Chirikov标准映射,其中混沌以非线性踢脚强度K增长,我们发现对于高K, Π(ρ ~)的行为类似于均匀双曲二维Cat映射。对于低K,我们看到具有类似于混沌制度的松弛包络的周期性行为,但频率取决于混合相空间中初始系综的大小和位置以及K。我们讨论Π(ρ≈)如何适应实验工作及其在量化复杂系统如何从低熵到高熵状态变化中的一般用途。
Permutation entropy of indexed ensembles: quantifying thermalization dynamics
We introduce ‘PI-Entropy’ Π(ρ˜) (the Permutation entropy of an Indexed ensemble) to quantify mixing due to complex dynamics for an ensemble ρ of different initial states evolving under identical dynamics. We find that Π(ρ˜) acts as an excellent proxy for the thermodynamic entropy S(ρ) but is much more computationally efficient. We study 1-D and 2D iterative maps and find that Π(ρ˜) dynamics distinguish a variety of system time scales and track global loss of information as the ensemble relaxes to equilibrium. There is a universal S-shaped relaxation to equilibrium for generally chaotic systems, and this relaxation is characterized by a shuffling timescale that correlates with the system’s Lyapunov exponent. For the Chirikov Standard Map, a system with a mixed phase space where the chaos grows with nonlinear kick strength K, we find that for high K, Π(ρ˜) behaves like the uniformly hyperbolic 2D Cat Map. For low K we see periodic behavior with a relaxation envelope resembling those of the chaotic regime, but with frequencies that depend on the size and location of the initial ensemble in the mixed phase space as well as K. We discuss how Π(ρ˜) adapts to experimental work and its general utility in quantifying how complex systems change from a low entropy to a high entropy state.