Current aerodynamic design processes suffer from expensive optimisation procedures, in
part due to the requirement to search large design spaces. Recent advances in deep …

Although learning low-dimensional airfoil manifolds can facilitate aerodynamic
optimizations, the properties of these latent spaces are not well understood. This paper …

In this study, we introduce a deep generative model, named Multi-Species Generative
Adversarial Network (MS-GAN), which is developed to extract the low-dimensional manifold …

Solving inverse problems in scientific and engineering fields has long been intriguing and
holds great potential for many applications, yet most techniques still struggle to address …

Many design problems involve reasoning about points in high-dimensional space. A
common strategy is to first embed these high-dimensional points into a low-dimensional …

The aerodynamic coefficients of aircrafts are significantly impacted by its geometry,
especially when the angle of attack (AoA) is large. In the field of aerodynamics, traditional …

In this paper, a novel mechanism-driven reinforcement learning framework is proposed for
airfoil shape optimization. To validate the framework, a reward function is designed and …

Computer-aided design (CAD) is a promising new area for the application of artificial
intelligence (AI) and machine learning (ML). The current practice of design of cyber-physical …

Effective optimisation of aerodynamic shapes requires high-quality parameterisation of
candidate geometries. In recent years, the increasing availability and applicability of data …

Airfoil optimization is critical to the design of turbine blades and aerial vehicle wings, among
other aerodynamic applications. This design process is often constrained by the …