2 citations · 5 across the 7 of their papers we have counts for
7 papers
Designing ship hull forms using generative adversarial networks
Kazuo Yonekura, Kotaro Omori, Xinran Qi +1
We proposed a GAN-based method to generate a ship hull form. Unlike mathematical hull forms that require geometrical parameters to generate ship hull forms, the proposed method req…
Airfoil generation and feature extraction using the conditional VAE-WGAN-gp
Kazuo Yonekura, Yuki Tomori, Katsuyuki Suzuki
A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and W…
Mixed variable structural optimization using mixed variable system Monte Carlo tree search formulation
Fu-Yao Ko, Katsuyuki Suzuki, Kazuo Yonekura
A novel method called mixed variable system Monte Carlo tree search (MVSMCTS) formulation is presented for optimization problems considering various types of variables with single…
Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures
Fu-Yao Ko, Katsuyuki Suzuki, Kazuo Yonekura
This paper proposes a novel reinforcement learning (RL) algorithm using improved Monte Carlo tree search (IMCTS) formulation for discrete optimum design of truss structures. IMCTS…
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…
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…