Dynamical low-rank (DLR) approximation methods have previously been developed for time-
dependent radiation transport problems. One crucial drawback of DLR is that it does not …

We present a method for accelerating discrete ordinates radiative transfer calculations for
radiative transfer. Our method works with nonlinear positivity fixes, in contrast to most …

In this work, we provide a fully-implicit implementation of the time-dependent, filtered
spherical harmonics (FP N) equations for non-linear, thermal radiative transfer. We …

This paper presents a positive and asymptotic preserving scheme for the nonlinear gray
radiative transfer equations. The scheme is constructed by combining the filtered spherical …

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 …

Reduced order models with a-priori unknown closure relations are ubiquitous in transport
problems. In this work, we present a machine-learning approach to finding closure relations …

The method of moments is widely used for the reduction of kinetic equations into fluid
models. It consists in extracting the moments of the kinetic equation with respect to a velocity …

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 …

Uncertainty quantification for hyperbolic equations is a challenging task, since solutions
exhibit discontinuities and sharp gradients. The commonly used stochastic-Galerkin (SG) …

We present a positive-and asymptotic-preserving numerical scheme for solving linear kinetic
transport equations that relax to a diffusive equation in the limit of infinite scattering. The …