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 …

Applications such as uncertainty quantification, shape optimization, and optical tomography,
require solving the radiative transfer equation (RTE) many times for various parameters …

A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative
transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low …

This article presents a new reduced order model (ROM) for the angular discretization of the
Boltzmann transport equation. The angular ROM is built over a partitioning of the space …

We propose a reduced-order model (ROM) based on dynamic mode decomposition (DMD)
for efficient reduced-order modeling of parameterized time-dependent partial differential …

A new group of structure and asymptotic preserving reduced-order models (ROMs) for
multidimensional nonlinear thermal radiative transfer (TRT) problems is presented. They are …

Kinetic transport equations are notoriously difficult to simulate because of their complex
multiscale behaviors and the need to numerically resolve a high-dimensional probability …

This article presents a new reduced order model (ROM) for fast solutions to neutron
transport problems. The novelty lies in the construction of optimal basis functions spanning …

When dealing with the steady-state multiscale radiative transfer equation (RTE) with
heterogeneous coefficients, spatially localized low-rank structures are present in the angular …