3 citations · 8 across the 5 of their papers we have counts for
4 papers · 1 filter
Physics-guided training of GAN to improve accuracy in airfoil design synthesis
Kazunari Wada, Katsuyuki Suzuki, Kazuo Yonekura
Generative adversarial networks (GAN) have recently been used for a design synthesis of mechanical shapes. A GAN sometimes outputs physically unreasonable shapes. For example, when…
Physics-guided generative adversarial network to learn physical models
Kazuo Yonekura
This short note describes the concept of guided training of deep neural networks (DNNs) to learn physically reasonable solutions. DNNs are being widely used to predict phenomena in…
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net
Kazuo Yonekura, Kento Maruoka, Kyoku Tyou +1
In a finite element analysis, using a large number of grids is important to obtain accurate results, but is a resource-consuming task. Aiming to real-time simulation and optimizati…
Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp
Kazuo Yonekura, Nozomu Miyamoto, Katsuyuki Suzuki
Machine learning models are recently utilized for airfoil shape generation methods. It is desired to obtain airfoil shapes that satisfies required lift coefficient. Generative adve…