Generating a digital twin of any complex system requires modeling and computational
approaches that are efficient, accurate, and modular. Traditional reduced order modeling …

Herein a new approach to enhance the accuracy of a novel Proper Orthogonal
Decomposition (POD) model applied to moderate Reynolds number flows (of the type …

We investigate the sensitivity of reduced order models (ROMs) to training data spatial
resolution as well as sampling rate. In particular, we consider proper orthogonal …

In this paper, we put forth an evolve-then-correct reduced order modeling approach that
combines intrusive and nonintrusive models to take hidden physical processes into account …

The necessity to include dynamic mean field representations in low order Galerkin models,
and the role and form of such representations, are explored along natural and forced …

Reduced-order models (ROM) of structurally dominated fluid flows have significant
applications in science and engineering, such as design, control, and optimization. Proper …

The role of energy and entropy in the decomposition of turbulent velocity flow-fields is shown
in this paper. Decomposition methods based on the energy concept are taken into account …

A key step to improve data-driven reduced-order simulations is to compute a transfer
function that predicts the time evolution of the reduced-order modes accurately. We …

Abstract Model reduction based on Galerkin projection is a key technique used in feedback
flow control. It significantly accelerates the flow computations, and thus it can be suitable for …

Low-dimensional models, allowing quick prediction of fluid behaviour, are key enablers of
closed-loop flow control. Reduction of the model's dimension and inconsistency of high …