Generalized thermostatistics and mean-field theory
Jan Naudts

TL;DR
This paper introduces a generalized thermostatistics framework based on deformed exponential and logarithmic functions, ensuring thermodynamic stability and broadening the applicability beyond Boltzmann-Gibbs distributions, with relevance to systems like the Ising chain.
Contribution
It develops a non-unique deformation approach to thermostatistics that guarantees stability and variational principles, connecting with and clarifying aspects of Tsallis' formalism.
Findings
Equilibrium states are thermodynamically stable and satisfy the variational principle.
Deformed exponential functions can model temperature-dependent distributions.
Application to the Ising chain demonstrates the theory's relevance.
Abstract
The present paper studies a large class of temperature dependent probability distributions and shows that entropy and energy can be defined in such a way that these probability distributions are the equilibrium states of a generalized thermostatistics. This generalized thermostatistics is obtained from the standard formalism by deformation of exponential and logarithmic functions. Since this procedure is non-unique, specific choices are motivated by showing that the resulting theory is well-behaved. In particular, the equilibrium state of any system with a finite number of degrees of freedom is, automatically, thermodynamically stable and satisfies the variational principle. The equilibrium probability distribution of open systems deviates generically from the Boltzmann-Gibbs distribution. If the interaction with the environment is not too strong then one can expect that a slight…
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.
