Affiliation:
1. Laboratory of Theoretical Physics of Condensed Matter, Faculty of Sciences, Sorbonne Université, Paris, France
2. Former Professor at the Faculty of Mathematics, Sorbonne Université, Paris, France
Abstract
<abstract> <p>Unless an appropriate dissipation mechanism is introduced in its evolution, a deterministic system generally does not tend to equilibrium. However, coarse-graining such a system implies a mesoscopic representation which is no longer deterministic. The mesoscopic system should be addressed by stochastic methods, but they lead to practically infeasible calculations. However, following the pioneering work of Kolmogorov, one finds that such mesoscopic systems can be approximated by Markov processes in relevant conditions, mainly, if the microscopic system is ergodic. So, the mesoscopic system tends to stationarity in specific situations, as expected from thermodynamics. Kolmogorov proved that in the stationary case, the instantaneous entropy of the mesoscopic process, conditioned by its past trajectory, tends to a finite limit at infinite times. Thus, one can define the Kolmogorov entropy. It can be shown that in certain situations, this property remains true even in the nonstationary case. We anticipated this important conclusion in a previous article, giving some elements of a justification, whereas it is precisely derived below in relevant conditions and in the case of a discrete system. It demonstrates that the Kolmogorov entropy is linked to basic aspects of time, such as its irreversibility. This extends the well-known conclusions of Boltzmann and of more recent researchers and gives a general insight to the fascinating relation between time and entropy.</p> </abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Reference25 articles.
1. V. I. Arnold, A. Avez, Ergodic problems of classical mechanics, Mathematical Physics Monographs, Benjamin, 1968.
2. B. Gaveau, M. Moreau, On the stochastic representation and Markov approximation of Hamiltonian systems, Chaos, 30 (2020), 083104. https://doi.org/10.1063/5.0001435
3. J. Doob, Stochastic processes, Wiley, New York, 1953.
4. P. Levy, Théorie de l'addition des variables aléatoires, Gauthier-Villars, Paris, 1937.
5. C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J., 27 (1948), 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x