Global optimization of aerodynamic shapes usually requires a large number of expensive
computational fluid dynamics simulations because of the high dimensionality of the design …

The authors present a generative adversarial network (GAN) model that demonstrates how
to generate 3D models in their native format so that they can be either evaluated using …

Humans as designers have quite versatile problem-solving strategies. Computer agents on
the other hand can access large-scale computational resources to solve certain design …

Deep generative models are proven to be a useful tool for automatic design synthesis and
design space exploration. When applied in engineering design, existing generative models …

Global optimization of aerodynamic shapes requires a large number of expensive CFD
simulations because of the high dimensionality of the design space. One means to combat …

We present a parametric approach for heat exchanger shape optimization utilizing Deep
Reinforcement Learning (Deep RL) and Boundary Representation (BREP). In this study, we …

Reliability analysis is a core element in engineering design and can be performed with
physical models (limit-state functions). Reliability analysis becomes computationally …

Real-world designs usually consist of parts with interpart dependencies, ie, the geometry of
one part is dependent on one or multiple other parts. We can represent such dependency in …

In this work, a data-driven technique is proposed for efficient design exploration and
optimisation of the Kaplan turbine. To avoid the curse of dimensionality, the proposed …

Many aerodynamic shape optimization methods often focus on utilizing the end-to-end
relationship between design variables and aerodynamic performance to find the optimal …