東京大学 · 工学
Kazuo Yonekura教授の研究室では、機械学習や深層ニューラルネットワークを活用した形状最適化と物理的制約を組み合わせた知能型設計技術の開発を進めています。特に空力形状（airfoil）の生成や予測において、生成モデル（VAE、GAN）と不確実性評価、物理則を統合する手法を応用し、実用的で信頼性の高い設計支援システムの構築を目指しています。また、非常に短時間の局所的天気予測の手法開発や、形状パラメータの次元削減手法の研究も並行して実施しています。
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This paper proposes a very-short-term, i.e., less than 1-hour, local weather forecast method. In general, a short-term weather forecast within 3 hours is difficult due to lack of surface weather data and limitations of computation resources. However, such a short-term prediction is getting more and more anticipated in several industrial situations such as transportation, retailing business, agriculture, and energy management as well as our daily life. To keep up with this huge demands, services
This paper proposes a shape parameterization method using a principal component analysis (PCA) for shape optimization. The proposed method is used as a preprocessing tool of parametric optimization algorithms, such as genetic algorithms (GAs) or response surface methods (RSMs). When these parametric optimization algorithms are used, the number of parameters should be small while the design space represented by the parameters should be able to represent a variety of shapes. In order to define the
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 Wasserstein generative adversarial network with gradient penalty (WGAN-gp), is proposed for an airfoil generation method, and then, it is compared with the WGAN-gp and VAE models. The VAEGAN model couples the VAE and GAN models, which enables feature extraction in the GAN models. In airfoil generation tasks, to generate
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