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 …

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

A data-driven framework is proposed towards the end of predictive modeling of complex
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …

Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …

This paper presents a new nonlinear non‐intrusive reduced‐order model (NL‐NIROM) that
outperforms traditional proper orthogonal decomposition (POD)‐based reduced order model …

Abstract Models with dominant advection always posed a difficult challenge for projection-
based reduced order modelling. Many methodologies that have recently been proposed are …

This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …

In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake characteristics, in this
paper we present high-fidelity CFD simulations of the flow around an exemplary H-shaped …

While data-driven model reduction techniques are well-established for linearizable
mechanical systems, general approaches to reducing nonlinearizable systems with multiple …