Large Deviations Principle for the Fluctuating Boltzmann Equation

TL;DR

This paper extends the classical Boltzmann equation into a stochastic framework, establishes a large deviations principle, and derives macroscopic hydrodynamic models, offering new insights into its probabilistic and Hamiltonian structures.

Contribution

It introduces a stochastic generalization of the Boltzmann equation, proves a large deviations principle, and derives hydrodynamic models under the BGK approximation.

Findings

Large deviations principle explains the probabilistic origin of the H-theorem.

Hamilton-Boltzmann equation maintains key physical properties.

Macroscopic hydrodynamic models with 13 moments are derived.

Abstract

The Boltzmann equation is one of the most famous equations and has vast applications in modern science. In the current study, we take the randomness of binary collisions into consideration and generalize the classical Boltzmann equation into a stochastic framework. The corresponding Kolmogorov forward equations and Liouville equation in either discrete or continuous time and state space are derived respectively, whose characteristic line gives the Boltzmann equation as a consequence of the law of large numbers. Then the large deviations principle for these equations is established, which not only explains the probabilistic origin of the H-theorem in the Boltzmann equation, but also provides a natural way to incorporate the Boltzmann equation into a broader Hamiltonian structure. The so-called Hamilton-Boltzmann equation enjoys many significant merits, like time reversibility, the conservation laws of mass, momentum and energy, Maxwellian-Boltzmann distribution as the equilibrium solution, etc. We also present results under the diffusive limit in parallel. Finally, the macroscopic hydrodynamic models including 13 moments are derived with respect to our Hamilton-Boltzmann equation under the BGK approximation. We expect our study can inspire new insights into the classical Boltzmann equation from either the stochastic aspect or a Hamiltonian view.