This article presents a comprehensive overview of the digital twin technology and its
capability levels, with a specific focus on its applications in the wind energy industry. It …

Numerical simulations have played a vital role in the design of modern combustion systems.
Over the last two decades, the focus of research has been on the development of the large …

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 …

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

Quantum computing uses the physical principles of very small systems to develop
computing platforms which can solve problems that are intractable on conventional …

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 …

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) …

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 …

Numerical simulation of fluid flows requires important computational efforts but it is essential
in engineering applications. Reduced Order Model (ROM) can be employed whenever fast …

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 …