The University of Tokyo · 공학
Kazuo Yonekura 교수의 연구실은 기계설계 및 공학 최적화 분야에서 데이터 기반의 지능형 설계 기법을 주요 연구 주제로 다룹니다. 특히, 생성적 적대 신경망(GAN), Variational Autoencoder(VAE), 그리고 확률적 최적화 기법을 활용해 공기역학적 형상(예: 에어포일)을 효율적으로 생성하고, 그 성능에 대한 불확실성까지 정량화하는 데 초점을 맞추고 있습니다. 또한, 물리 법칙을 통합한 신경망 설계 및 매우 단기 기상 예측을 위한 고밀도 센서 기반 모델링도 함께 진행 중입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
