• twitter
  • facebook
  • instagram
  • Youtube
  • GitHub
  • Box
  • Qiita
MENU

MEMBERS

Muramatsu, M., Kishimoto, H., Yamaji, K., Yahiro, K., Kawada, T. and Terada, K., “Electro-chemical Potential Analysis of Zirconium Based on The Reaction-diffusion Equations of Oxygen Ion and Electron Considering Phase Transformation”, ECS Transactions, Vol. 68, No. 1, pp. 2363-2372, (2015).https://doi.org/10.1149/06801.2363ecst

Achievement of Kenta Hirayama

Paper

  1. Kojima, Y., Hirayama, K., Harada, Y., Muramatsu, M., “Discussion on infrared stress measurements based on finite element analysis of transient heat conduction”, Bulletin of the JSME, ID23-00571, pp. 1-12, (2024).https://doi.org/10.1299/mej.23-00571
  2. Sasaki, K., Hirayama, K., Endo, K., Muramatsu, M., Murayama, M., “Nanoscale Defect Evaluation Framework Combining Real-Time Transmission Electron Microscopy and Integrated Machine Learning-Particle Filter Estimation”, Scientific Reports, Vol. 12, pp. 10525, 1-10, (2022).https://doi.org/10.1038/s41598-022-13878-8
  3. Kojima, Y., Hirayama, K., Endo, K., Hiraide, K., Muramatsu, M., “Inverse Estimation Method for Internal Defects Based on Surface Stress of Carbon-Fiber-Reinforced Plastics Using Machine Learning”, Advanced Composite Materials, Vol. 31, pp. 617-629, (2022).https://doi.org/10.1080/09243046.2022.2052786
  4. Hiraide, K., Hirayama, K., Endo, K., Muramatsu, M., “Application of deep learning to inverse design of phase separation structure in polymer alloy”, Computational Materials Science, Vol. 190, pp. 110278, 1-9, (2021).https://doi.org/10.1016/j.commatsci.2021.110278

International conference

  1. Kojima, Y.*，Hirayama，K., Endo，K., Hiraide, K., Muramatsu, M., Harada, Y., “Defects Analysis in Carbon Fiber Reinforced Plastic by Combining Machine Learning and Infrared Stress Analysis”, 15th World Congress on Computation Mechanics & 8th Asian Pacific Congress on Computation Mechanics (WCCM-APCOM2022), Online (2022), MS1713, 2817.
  2. Kojima, Y.*, Hiraide, K., Hirayama, K., Endo, K., Muramatsu, M., Harada, Y. “Development of the Defects Detection System for Carbon Fiber Reinforced Plastic by Using Infrared Stress Analysis and Machine Learning”, 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS2022), Norway (2022), MS117D-1.
  3. Sasaki, K.*, Hirayama, K., Endo, K., Murayama, M., Muramatsu, M. “Dislocation Detection and Velocity Measurement During Deformation of Metals Using Machine Learning and Particle Filter”, XVI International Conference on Computational Plasticity (COMPLAS2021), Online, (2021), WM12-3.
  4. Kojima, Y.*, Hiraide, K., Hirayama, K., Endo, K., Muramatsu, M. “Estimation of Defects in Carbon Fiber Reinforced Plastic Based on Surface Pressure by Machine Learning”, XVI International Conference on Computational Plasticity (COMPLAS2021), Online, (2021), WM12-4.
  5. Hiraide, K.*, Hirayama, K., Endo, K., Muramatsu, M. “Inverse design of polymer alloy using machine learning based on macroscopic property”, 14th World Congress in Computational Mechanics and ECCOMAS (WCCM2020), Online, (2020), 3004.
  6. Hiraide, K.*, Hirayama, K., Endo, K., Muramatsu, M., “Inverse design of polymer using machine learning based on macroscopic property”, 3rd International Conference on Computational Engineering and Science for Safety and Environmental Problems (COMPSAFE2020), Online, (2020), MS23-2-04.
  7. Hirayama, K.*, Kondo, R., Muramatsu, M., “Development of a machine learning method for the prediction of dendrite formation”, 3rd International Conference on Computational Engineering and Science for Safety and Environmental Problems (COMPSAFE2020), Online, (2020), MS23-2-01.
  8. Hirayama, K.*, Endo, K., Yasuoka, K., Muramatsu, M., “Prediction of Spinodal Decomposition by Machine Learning Method”, Abstract of Asian Pacific Congress on Computational Mechanics (APCOM 2019), in Taipei, Taiwan, (2019), 0681.

Domestic conference

  1. 藤原紳也*, 岡田清志郎, 遠藤克浩, 平山健太, 村松眞由「Phase-field法における乱数がデンドライト構造に与える影響の検討｣, 第28回計算工学講演会, B-04-01 (6 pages), (2023).
  2. 児嶋佑太*，遠藤克浩，平山健太, 原田祥久，村松眞由「有限要素解析および赤外線測定による応力分布に基づく転移学習を用いたCFRP 単純形状内の欠陥3次元情報の予測｣, 第28回計算工学講演会, A-11-01 (6 pages), (2023).
  3. 児嶋佑太*，遠藤克浩，平山健太，平出和也，村松眞由, 原田祥久「機械学習と赤外線応力測定を組み合わせた炭素繊維強化プラスチックの欠陥探索｣, 第27回計算工学講演会, C-07-02 (6 pages), (2022).
  4. 村松眞由*，平出和也，平山健太，遠藤克浩，大矢豊大, 「ジブロックポリマー相分離構造の自己無撞着場理論解析と機械学習モデルの構築｣, 第26回計算工学講演会, B-07-01 (6 pages), (2021).
  5. 児嶋佑太*，遠藤克浩，平山健太，平出和也，村松眞由, 「機械学習を用いた炭素繊維強化プラスチック表面応力に基づく内部欠陥逆推定｣, 第26回計算工学講演会, B-07-03 (6 pages), (2021).
  6. 佐々木翔唯*，平山健太，遠藤克浩，村山光宏，村松眞由, 「機械学習およびパーティクルフィルタを用いた変形金属のTEM観察動画中の転位検出および移動速度測定｣, 第26回計算工学講演会, B-07-02 (6 pages), (2021).(第26回日本計算工学講演会 若手優秀講演フェロー表彰)
  7. 平出和也*， 平山健太， 遠藤克浩，村松眞由, ｢ポリマーアロイ相分離構造の逆解析への深層学習の導入｣, 日本機械学会関東支部第27期 総会・講演会, OS10-2-11C05 (4 pages), (2021).
  8. 佐々木翔唯*，平山健太，遠藤克浩，村山光宏，村松眞由， ｢機械学習を用いた変形金属における転位検出および移動速度測定ツールの開発｣, 日本機械学会関東学生会第60回学生員卒業研究発表講演会, OS4-419 (5 pages), (2021).
  9. 児嶋佑太*，村松眞由，遠藤克浩，平山健太，平出和也， ｢機械学習を用いた層間剝離を有する炭素繊維強化プラスチックの表面応力・欠陥解析｣, 日本機械学会関東学生会第60回学生員卒業研究発表講演会, OS4-418 (5 pages), (2021).
  10. 平出 和也*，平山 健太，遠藤 克浩，村松 眞由, ｢深層学習によるポリマーアロイ相分離構造の逆解析手法の提案｣, 第25回計算工学講演会, B-07-04 (6 pages), (2020).
  11. 平山健太*，遠藤克浩，泰岡顕治，村松眞由, ｢機械学習によるスピノーダル分解のコンディション付き予測｣, 第32回日本機械学会計算力学講演会講演論文集, OS19-3-194 (4 pages), (2019).
  12. 平山健太*，遠藤克浩，村松眞由，泰岡顕治, ｢機械学習によるスピノーダル分解の予測手法の提案｣, 第24回計算工学講演会, C-11-04 (6 pages), (2019).
Back to MEMBERS INDEX