Nonequilibrium thermodynamics (NET) investigates processes in systems out of global
equilibrium. On a mesoscopic level, it provides a statistical dynamic description of various …

In this paper, we develop a method for finding models of heat conduction via machine
learning. Integrating machine learning and the conservation-dissipation formulism (CDF) of …

We design and compute first-order implicit-in-time variational schemes with high-order
spatial discretization for initial value gradient flows in generalized optimal transport metric …

In this paper, we take a data-driven approach and apply machine learning to the moment
closure problem for the radiative transfer equation in slab geometry. Instead of learning the …

This is the second paper in a series in which we develop machine learning (ML) moment
closure models for the radiative transfer equation (RTE). In our previous work [J. Huang, Y …

In this paper, we summarize some recent advances related to the energetic variational
approach (EnVarA), a general variational framework of building thermodynamically …

The lack of formulation of macroscopic equations for irreversible dynamics of viscous heat-
conducting media compatible with the causality principle of Einstein's special relativity and …

In this work, we develop a method for learning interpretable, thermodynamically stable and
Galilean invariant partial differential equations (PDEs) based on the conservation …

This is the third paper in a series in which we develop machine learning (ML) moment
closure models for the radiative transfer equation. In our previous work (Huang et al. in J …

Many macroscopic non-Fourier heat conduction models have been developed in the past
decades based on Chapman-Enskog, Hermite, or other small perturbation expansion …