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

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 …

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

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 …

Training neural networks sequentially in time to approximate solution fields of time-
dependent partial differential equations can be beneficial for preserving causality and other …

Inspired by our previous work on a quadratic approximation manifold [1], we propose in this
paper a computationally tractable approach for combining a projection-based reduced-order …