This paper describes the application of generative adversarial networks (GANs) to airfoil
inverse design. Specifically, this work focuses on creating new airfoil shapes via conditional …

Shape optimization is an indispensable step in any aerodynamic design. However, the
inherent complexity and non-linearity associated with fluid mechanics as well as the high …

In this study, we propose an encoder–decoder convolutional neural network-based
approach for estimating the pressure field around an airfoil. The developed tool is one of the …

A deep learning framework is proposed for real-time transonic flow prediction. To capture
the complex shock discontinuity of transonic flow, we introduce the residual network ResNet …

Micro-Doppler signatures obtained from the Doppler radar are generally used for human
activity classification. However, if the angle between the direction of motion and radar …

Abstract Machine learning models are recently adopted to generate airfoil shapes. A typical
task is to obtain airfoil shapes that satisfy the required lift coefficient. These inverse design …

R years have witnessed the success of deep learning [1] in many fields like computer vision
[2], natural language process [3] and robotics [4][5]. Such data-driven methods can …

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds
number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar …

The current design of aerodynamic shapes, like airfoils, involves computationally intensive
simulations to explore the possible design space. Usually, such design relies on the prior …

A general and flexible approximation model based on convolutional neural network
(ConvNet) technique as well as a signed distance function (SDF) is proposed to predict …