SIMPLE aerodynamic configurations under even modest conditions can exhibit complex
flows with a wide range of temporal and spatial features. It has become common practice in …

Advances in experimental techniques and the ever-increasing fidelity of numerical
simulations have led to an abundance of data describing fluid flows. This review discusses a …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

Dynamic mode decomposition (DMD) is an Arnoldi-like method based on the Koopman
operator. It analyzes empirical data, typically generated by nonlinear dynamics, and …

We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …

We propose a customized convolutional neural network based autoencoder called a
hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow …

In this work we present a Reduced Order Model which is specifically designed to deal with
turbulent flows in a finite volume setting. The method used to build the reduced order model …

Abstract Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …

We present a new framework for optimal and feedback control of PDEs using Koopman
operator-based reduced order models (K-ROMs). The Koopman operator is a linear but …

In this work a stabilised and reduced Galerkin projection of the incompressible unsteady
Navier–Stokes equations for moderate Reynolds number is presented. The full-order model …