Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

During the past few decades, several significant progresses have been made in exploring
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …

The traditional method for obtaining aerodynamic parameters of airfoils by solving Navier–
Stokes equations is a time-consuming computing task. In this article, a novel data-driven …

Learning from data offers new opportunities for develo** computational methods in
research fields, such as fluid dynamics, which constantly accumulate a large amount of data …

Understanding and accurately predicting complex three-dimensional (3D) temperature
distributions are critical in diverse domains, including climate science and industrial process …

The flow field under different flow conditions contains abundant structure information and is
of great significance for aerodynamic analysis and aircraft design. Deep learning (DL) …

The computational fluid dynamics (CFD) method is computationally intensive and costly, and
evaluating aerodynamic performance through CFD is time-consuming and labor-intensive …

Airfoil design and surrogate modeling for performance prediction based on deep learning
method | Physics of Fluids | AIP Publishing Skip to Main Content Umbrella Alt Text Umbrella Alt …

In aerodynamic shape optimization, a high-fidelity (HF) simulation is generally more
accurate but more time-consuming than a low-fidelity (LF) simulation. To take advantage of …

In this paper, a deep learning framework is proposed for predicting aerodynamic pressure
distributions in general three-dimensional configurations. Based on the PointNet++ …