Reduced order models (ROMs) are computational models whose dimension is significantly
lower than those obtained through classical numerical discretizations (eg, finite element …

In this study, we present a nonintrusive reduced order modeling (ROM) framework for large-
scale quasistationary systems. The framework proposed herein exploits the time series …

In this article we detail the use of machine learning for spatio-temporally dynamic turbulence
model classification and hybridization for large eddy simulations (LES) of turbulence. Our …

We investigate the application of artificial neural networks to stabilize proper orthogonal
decomposition-based reduced order models for quasistationary geophysical turbulent flows …

We propose a new data-driven reduced order model (ROM) framework that centers around
the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data …

We propose an improved framework for dynamic mode decomposition (DMD) of 2‐D flows
for problems originating from meteorology when a large time step acts like a filter in …

Two parameterizations for horizontal mixing of momentum and tracers by subgrid mesoscale
eddies are implemented in a high-resolution global ocean model. These parameterizations …

Although the two-layer quasi-geostrophic equations (2QGE) are a simplified model for the
dynamics of a stratified, wind-driven ocean, their numerical simulation is still plagued by the …

In this paper, a stabilized proper orthogonal decomposition (POD) reduced-order model
(ROM) framework is developed for the barotropic vorticity equation. Two different closure …

This paper investigates the recently introduced data-driven correction reduced order model
(DDC-ROM) in the numerical simulation of the quasi-geostrophic equations. The DDC-ROM …