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 …

We develop a general framework for data-driven approximation of input-output maps
between infinitedimensional spaces. The proposed approach is motivated by the recent …

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …

Computational fluid dynamics (CFD) models have been used for the simulation of urban
airflow and pollutant dispersion, due to their capability to capture different length scales and …

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) …

Reduced order modeling (ROM) has been widely used to create lower order,
computationally inexpensive representations of higher-order dynamical systems. Using …

Highly accurate numerical or physical experiments are often very time-consuming or
expensive to obtain. When time or budget restrictions prohibit the generation of additional …

Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …

Inspired by our previous work on a quadratic approximation manifold [1], we propose in this
paper a computationally tractable approach for combining a projection-based reduced-order …

This work deals with the investigation of bifurcating fluid phenomena using a reduced order
modelling setting aided by artificial neural networks. We discuss the POD-NN approach …