In the last two decades, the use of OpenFOAM as a multi-physics library for nuclear
applications has grown from a sporadic use for exploratory studies to a widespread …

This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …

This work presents a projection-based multiphysics Model Order Reduction (MOR)
framework for the analysis of nuclear systems and its application to parametric simulations of …

Using an autoencoder for dimensionality reduction, this article presents a novel projection‐
based reduced‐order model for eigenvalue problems. Reduced‐order modeling relies on …

We present a parametric reduced-order model for the neutral particle radiation transport
equation. The approach devised is a minimally-intrusive, projection-based reduced-order …

Due to the high-dimensional integro-differential properties of the neutron transport equation
(NTE) and the large-scale property of the nuclear reactor system, the detailed neutron …

In this paper, a proper orthogonal decomposition based reduced-order model is presented
for parametrized multiphysics computations. Our application physics is Doppler feedback in …

We present a parametric, non-intrusive reduced-order model (ROM) for multigroup radiation
transport, addressing the high computational cost and resource requirements associated …

We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced
model for a molten salt reactor system. Our approach is based on Proper Orthogonal …

This work presents a data-driven, projection-based parametric reduced-order model (ROM)
for the neutral particle radiation transport (linear Boltzmann transport) equation. The ROM …