Despite tremendous progress seen in the computational fluid dynamics community for the
past few decades, numerical tools are still too slow for the simulation of practical flow …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

This paper reviews studies on neural networks in aerodynamic data modeling. In this paper,
we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced …

Abstract We present Lift & Learn, a physics-informed method for learning low-dimensional
models for large-scale dynamical systems. The method exploits knowledge of a system's …

This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …

This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …

A data-driven reduced basis (RB) method for parametrized time-dependent problems is
proposed. This method requires the offline preparation of a database comprising the time …

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 …

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 proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …