In this paper, we explore applications of deep learning in statistical physics. We choose the
Boltzmann equation as a typical example, where neural networks serve as a closure to its …

We introduce a framework for generating samples of a distribution given a finite number of
its moments, targeted at particle-based solutions of kinetic equations and rarefied gas flow …

The uncertainty of surrounding rock mechanical parameters has much great influence on
design, construction and stability evaluation for tunnel engineering. However, the traditional …

This work presents neural network based minimal entropy closures for the moment system of
the Boltzmann equation, that preserve the inherent structure of the system of partial …

We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic
densitY estimation method. The proposed approach recovers probability density functions …

We present a data-driven approach to construct entropy-based closures for the moment
system from kinetic equations. The proposed closure learns the entropy function by fitting the …

An important class of multi-scale flow scenarios deals with an interplay between kinetic and
continuum phenomena. While hybrid solvers provide a natural way to cope with these …

We present a novel framework, enabled by machine learning (ML) trainable models, for
collisional plasma flow simulations governed by the Rosenbluth-Fokker-Planck (RFP) …

We survey a number of moment hierarchies and test their performances in computing one-
dimensional shock structures. It is found that for high Mach numbers, the moment …

Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In
this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) …