This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

This review article is concerned with the design of linear reduced-order models and control
laws for closed-loop control of instabilities in transitional flows. For oscillator flows, such as …

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 …

We propose a data-driven filtered reduced order model (DDF-ROM) framework for the
numerical simulation of fluid flows. The novel DDF-ROM framework consists of two steps:(i) …

Reduced order models (ROMs) are computational models whose dimension is significantly
lower than those obtained through classical numerical discretizations (eg, finite element …

In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to
account for hidden physics in reduced order modeling (ROM) of parameterized systems …

A novel parameterized non-intrusive reduced order model (P-NIROM) based on proper
orthogonal decomposition (POD) has been developed. This P-NIROM is a generic and …

A novel non-intrusive reduced order model (NIROM) for fluid–structure interaction (FSI) has
been developed. The model is based on proper orthogonal decomposition (POD) and radial …

We propose a new data-driven reduced order model (ROM) framework that centers around
the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data …

We have recently proposed a data‐driven correction reduced‐order model (DDC‐ROM)
framework for the numerical simulation of fluid flows, which can be formally written as …