The proposal that core collapse supernovae are neutrino driven is still the subject of active
investigation more than 50 years after the seminal paper by Colgate and White. The modern …

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 …

The dynamical low-rank approximation (DLRA) is used to treat high-dimensional problems
that arise in such diverse fields as kinetic transport and uncertainty quantification. Even …

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 …

The radiative transfer equation (RTE) for a plane-parallel problem involving scattering,
absorption and radiation is solved using the discontinuous Galerkin (DG) finite element …

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 …

The integro-differential radiative transfer equation (RTE) for concentric cylinders problem
involving scattering, absorption and emission is solved using the discontinuous Galerkin …

We present a hybrid method for time-dependent particle transport problems that combines
Monte Carlo (MC) estimation with deterministic solutions based on discrete ordinates. For …

In this paper, we study the linear transport model by adopting the deep learning method, in
particular the deep neural network (DNN) approach. While the interest of using DNN to study …

We present an arbitrarily high-order, conditionally stable, partitioned spectral deferred
correction (SDC) method for solving multiphysics problems using a sequence of pre-existing …