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来源类型Article
规范类型其他
DOI10.3390/e20110838
Maximum Configuration Principle for Driven Systems with Arbitrary Driving.
Hanel R; Thurner S
发表日期2018
出处Entropy 20 (11): p. 838
出版年2018
语种英语
摘要Depending on context, the term entropy is used for a thermodynamic quantity, a measure of available choice, a quantity to measure information, or, in the context of statistical inference, a maximum configuration predictor. For systems in equilibrium or processes without memory, the mathematical expression for these different concepts of entropy appears to be the so-called Boltzmann–Gibbs–Shannon entropy, H. For processes with memory, such as driven- or self- reinforcing-processes, this is no longer true: the different entropy concepts lead to distinct functionals that generally differ from H. Here we focus on the maximum configuration entropy (that predicts empirical distribution functions) in the context of driven dissipative systems. We develop the corresponding framework and derive the entropy functional that describes the distribution of observable states as a function of the details of the driving process. We do this for sample space reducing (SSR) processes, which provide an analytically tractable model for driven dissipative systems with controllable driving. The fact that a consistent framework for a maximum configuration entropy exists for arbitrarily driven non-equilibrium systems opens the possibility of deriving a full statistical theory of driven dissipative systems of this kind. This provides us with the technical means needed to derive a thermodynamic theory of driven processes based on a statistical theory. We discuss the Legendre structure for driven systems.
主题Advanced Systems Analysis (ASA)
关键词non-equilibrium maximum configuration maximum entropy principle driven systems statistical mechanics
URLhttp://pure.iiasa.ac.at/id/eprint/15623/
来源智库International Institute for Applied Systems Analysis (Austria)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/131257
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Hanel R,Thurner S. Maximum Configuration Principle for Driven Systems with Arbitrary Driving.. 2018.
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