The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

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

Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …

This paper presents a novel model reduction method: deep learning reduced order model,
which is based on proper orthogonal decomposition and deep learning methods. The deep …

We present a numerical methodology for construction of reduced-order models (ROMs) of
fluid flows through the combination of flow modal decomposition and regression analysis …

We propose a general dynamic reduced-order modelling framework for typical experimental
data: time-resolved sensor data and optional non-time-resolved particle image velocimetry …

We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady
flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt …

This paper proposes a supervised machine learning framework for the non-intrusive model
order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state …