PhilSci Archive

On the foundations of statistical mechanics: ergodicity, many degrees of freedom and inference

Chibbaro, Sergio and Rondoni, Lamberto and Vulpiani, Angelo (2014) On the foundations of statistical mechanics: ergodicity, many degrees of freedom and inference. [Preprint]

[img]
Preview
PDF (13 pages)
kitpc-CRV-revised.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (300kB)

Abstract

The present paper is meant to give a simple introduction to the problem of
the connection between microscopic dynamics and statistical laws. For sake of
simplicity, we mostly refer to non-dissipative dynamics, since dissipation adds
technical difficulties to the conceptual issues, although part of our
discussion extends beyond this limit. In particular, the relevance of chaos and
ergodicity is here confronted with that of the large number of degrees of
freedom. In Section 2, we review the microscopic connection, along the lines of
Boltzmann's approach, and of its further developments. In Section 3, we discuss
the falsifiability of statistical mechanics and its role as statistical
inference. In particular we argue that the Maximum entropy priciple is in
general not a predictive tool.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Chibbaro, Sergiosergio.chibbaro@upmc.fr
Rondoni, Lamberto
Vulpiani, Angelo
Additional Information: Published on Communications in Theoretical Physics Vol. 62, No. 4, pp. 469--475, 2014
Keywords: ergodic problem, statistical mechanics, Khinchin., maximum entropy principle
Subjects: Specific Sciences > Physics > Statistical Mechanics/Thermodynamics
Depositing User: Dr Sergio Chibbaro
Date Deposited: 02 Oct 2014 14:29
Last Modified: 02 Oct 2014 14:29
Item ID: 11052
Subjects: Specific Sciences > Physics > Statistical Mechanics/Thermodynamics
Date: September 2014
URI: https://philsci-archive.pitt.edu/id/eprint/11052

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item