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

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 …

In the present work, an efficient active flow control strategy in eliminating vortex-induced
vibration of a cylinder at Re= 100 has been explored by two machine learning frameworks …

Many reduced-order models are neither robust with respect to parameter changes nor cost-
effective enough for handling the nonlinear dependence of complex dynamical systems. In …

We put forth a data-driven closure modeling approach for stabilizing projection based
reduced order models for the Bousinessq equations. The effect of discarded modes is taken …

Transonic buffet is a phenomenon of aerodynamic instability with shock wave motions which
occurs at certain combinations of Mach number and mean angle of attack, and which limits …

In this paper, we propose a new evolve‐then‐filter reduced order model (EF‐ROM). This is a
regularized ROM (Reg‐ROM), which aims to add numerical stabilization to proper …

Reynolds-averaged Navier–Stokes (RANS)-based data assimilation has proven to be
essential in many data-driven approaches, including the augmentation of experimental data …

This paper proposes a large eddy simulation reduced order model (LES-ROM) framework
for the numerical simulation of convection-dominated flows. In this LES-ROM framework, the …

This work reports the application of a model-free deep reinforcement learning (DRL) based
flow control strategy to suppress perturbations evolving in the one-dimensional linearised …