This paper proposes a novel approach for learning a data-driven quadratic manifold from
high-dimensional data, then employing this quadratic manifold to derive efficient physics …

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) …

Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …

In the field of fluid mechanics, it is a potential consensus that nonlinear dimensionality
reduction (DR) techniques outperform linear methods. However, this conclusion has been …

Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of
the model of interest can result in a slow Kolmogorov n-width decay, which precludes the …

Abstract The Rayleigh-Taylor instability is a classical hydrodynamic instability of great
interest in various disciplines of science and engineering, including astrophysics …

When the water diversion mechanism fails to operate normally during the frequent mode
transitions in a pumped storage power station, the unit may enter into a runaway condition …

For parametric high-fidelity simulations, it is often desirable to utilize a reduced-order model
(ROM) to emulate, at a reduced computational cost, parametric solutions of the governing …

Many real-world applications demand accurate and fast predictions, as well as reliable
uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is …

Predicting the flow situation of cavitation owing to its high-dimensional nonlinearity has
posed great challenges. To address these challenges, this study presents a novel reduced …