Dynamics and Thermodynamics of the mean field d-HMF model out-of-equilibrium
Boris Atenas

TL;DR
This paper investigates the out-of-equilibrium dynamics and thermodynamics of the d-HMF model, revealing ensemble equivalence, Boltzmann-Gibbs equilibrium distributions, and characterizing quasi-stationary states using molecular dynamics and Vlasov equation solutions.
Contribution
It provides analytical and numerical descriptions of the d-HMF model's equilibrium and out-of-equilibrium states, including the characterization of QSS via stationary Vlasov solutions.
Findings
Ensemble equivalence established between canonical and microcanonical.
Boltzmann-Gibbs distribution matches simulations.
QSS described by q-exponential stationary solutions.
Abstract
We study the d-HMF model proposed by Atenas and Curilef, a mean field model with long-range interactions inspired by the dipole-dipole interaction. Among the challenges of this thesis is: the resolution of the d-HMF model in the canonical and microcanonical ensembles and the analytical and numerical description of the distribution function of the system. This model has been studied both in equilibrium and out of equilibrium. In the equilibrium, analytical solutions have been found for internal energy per particle and temperature using standard procedures through statistical mechanics such as the calculation of the partition function in the canonical ensemble and the calculation of the number of microstates accessible in the microcanonical ensemble. The results indicate that there is an equivalence of ensembles. Additionally, we found the Boltzmann-Gibbs (BG) equilibrium distribution,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Mechanics and Entropy · Advanced Thermodynamics and Statistical Mechanics · Complex Systems and Time Series Analysis
