The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective
field theory) presents a pedagogical introduction to projection-based, reduced-order …

The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …

One approach to parametric and adaptive model reduction is via the interpolation of
orthogonal bases, subspaces or positive definite system matrices. In all these cases, the …

The field of model order reduction (MOR) is growing in importance due to its ability to extract
the key insights from complex simulations while discarding computationally burdensome …

We present the results of a National Science Foundation Project Scoping Workshop, the
purpose of which was to assess the current status of calculations for the nuclear matrix …

In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake characteristics, in this
paper we present high-fidelity CFD simulations of the flow around an exemplary H-shaped …

We present a comprehensive investigation of few-nucleon systems as well as light and
medium-mass nuclei up to A= 48 using the current Low Energy Nuclear Physics …

In this paper, we present a deep learning technique for data-driven predictions of wave
propagation in a fluid medium. The technique relies on an attention-based convolutional …

Abstract We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced
Order Model (ROM) for an implementation of the Leray model that combines a two-step …

Abstract We develop a Reduced Order Model (ROM) for the Navier-Stokes equations with
nonlinear filtering stabilization. Our approach, that can be interpreted as a Large Eddy …