| Framework for design optimization using deep reinforcement learning K Yonekura, H Hattori Structural and Multidisciplinary Optimization 60, 1709-1713, 2019 | 59 | 2019 |
| Isotropic Ti–6Al–4V lattice via topology optimization and electron-beam melting A Takezawa, K Yonekura, Y Koizumi, X Zhang, M Kitamura Additive Manufacturing 22, 634-642, 2018 | 57 | 2018 |
| Second-order cone programming with warm start for elastoplastic analysis with von Mises yield criterion K Yonekura, Y Kanno Optimization and Engineering 13, 181-218, 2012 | 53 | 2012 |
| Global optimization of robust truss topology via mixed integer semidefinite programming K Yonekura, Y Kanno Optimization and Engineering 11, 355-379, 2010 | 52 | 2010 |
| Data-driven design exploration method using conditional variational autoencoder for airfoil design K Yonekura, K Suzuki Structural and Multidisciplinary Optimization 64, 613-624, 2021 | 48 | 2021 |
| Short-term local weather forecast using dense weather station by deep neural network K Yonekura, H Hattori, T Suzuki 2018 IEEE international conference on big data (big data), 1683-1690, 2018 | 39 | 2018 |
| A shape parameterization method using principal component analysis in applications to parametric shape optimization K Yonekura, O Watanabe Journal of Mechanical Design 136 (12), 121401, 2014 | 28 | 2014 |
| Film cooling hole shape optimization using proper orthogonal decomposition K Nita, Y Okita, C Nakamata, S Kubo, K Yonekura, O Watanabe Volume 2B: Turbomachinery, 2014 | 28 | 2014 |
| A flow topology optimization method for steady state flow using transient information of flow field solved by lattice Boltzmann method K Yonekura, Y Kanno Structural and Multidisciplinary Optimization 51 (1), 159-172, 2014 | 27 | 2014 |
| Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp K Yonekura, N Miyamoto, K Suzuki Structural and Multidisciplinary Optimization 65 (6), 173, 2022 | 21 | 2022 |
| Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders K Yonekura, K Wada, K Suzuki Engineering Applications of Artificial Intelligence 108, 104560, 2022 | 21* | 2022 |
| Turbine blade N Kozo, Y Okita, C Nakamata, K Yonekura, S Kubo, O Watanabe US Patent 9,759,069, 2017 | 14 | 2017 |
| Large-scale CAMUI type hybrid rocket motor scaling, modeling, and test results T Viscor, L Kamps, K Yonekura, H Isochi, H Nagata Aerospace 9 (1), 1, 2021 | 12 | 2021 |
| Topology optimization method for interior flow based on transient information of the lattice Boltzmann method with a level-set function K Yonekura, Y Kanno Japan Journal of Industrial and Applied Mathematics 34, 611-632, 2017 | 11 | 2017 |
| Erratum to: A flow topology optimization method for steady state flow using transient information of flow field solved by lattice Boltzmann method K Yonekura, Y Kanno Structural and Multidisciplinary Optimization 54, 193-195, 2016 | 7 | 2016 |
| A Heuristic Method Using Hessian Matrix for Fast Flow Topology Optimization K Yonekura, Y Kanno Journal of Optimization Theory and Applications 180 (2), 671–681, 2019 | 6 | 2019 |
| Super-resolving 2D stress tensor field conserving equilibrium constraints using physics-informed U-Net K Yonekura, K Maruoka, K Tyou, K Suzuki Finite Elements in Analysis and Design 213, 103852, 2023 | 4 | 2023 |
| Physics-guided training of GAN to improve accuracy in airfoil design synthesis K Wada, K Suzuki, K Yonekura Computer Methods in Applied Mechanics and Engineering 421, 116746, 2024 | 3 | 2024 |
| Detection of gait variations by using artificial neural networks C Guzelbulut, S Shimono, K Yonekura, K Suzuki Biomedical engineering letters 12 (4), 369-379, 2022 | 3 | 2022 |
| 機械学習と CAE を利用したターボ機械の設計支援技術 斉藤弘樹， 服部均， 米倉一男 IHI 技報 59 (1), 30-43, 2019 | 3 | 2019 |
Yoshihiro KannoTokyo Institute of TechnologyVerified email at serc.titech.ac.jp
