Article
Version 1
Preserved in Portico This version is not peer-reviewed
Far-from-Equilibrium Time Evolution between two Gamma Distributions
Version 1
: Received: 18 August 2017 / Approved: 21 August 2017 / Online: 21 August 2017 (12:11:18 CEST)
A peer-reviewed article of this Preprint also exists.
Kim, E.-J.; Tenkès, L.-M.; Hollerbach, R.; Radulescu, O. Far-From-Equilibrium Time Evolution between Two Gamma Distributions. Entropy 2017, 19, 511. Kim, E.-J.; Tenkès, L.-M.; Hollerbach, R.; Radulescu, O. Far-From-Equilibrium Time Evolution between Two Gamma Distributions. Entropy 2017, 19, 511.
Abstract
Many systems in nature and laboratories are far from equilibrium and exhibit significant fluc- tuations, invalidating the key assumptions of small fluctuations and short memory time in or near equilibrium. A full knowledge of Probability Distribution Functions (PDFs), especially time- dependent PDFs, becomes essential in understanding far-from-equilibrium processes. We consider a stochastic logistic model with multiplicative noise, which has gamma distributions as stationary PDFs. We numerically solve the transient relaxation problem, and show that as the strength of the stochastic noise increases the time-dependent PDFs increasingly deviate from gamma distributions. For sufficiently strong noise a transition occurs whereby the PDF never reaches a stationary state, but instead forms a peak that becomes ever more narrowly concentrated at the origin. The addition of an arbitrarily small amount of additive noise regularizes these solutions, and re-establishes the existence of stationary solutions. In addition to diagnostic quantities such as mean value, standard deviation, skewness and kurtosis, the transitions between different solutions are analyzed in terms of entropy and information length, the total number of statistically distinguishable states that a system passes through in time.
Keywords
non-equlibrium; stochastic systems; langevin equation; fokker-planck equation; time-dependent PDFs; gamma distribution
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment