Kojima, Y., Hirayama, K., Harada, Y., Muramatsu, M., “Transfer-learning-aided Defect Prediction in Simply Shaped CFRP Specimens Based on Stress Distribution Obtained from Finite Element Analysis and Infrared Stress Measurement”, Composites Part B, Vol. ??, pp. ??-??, (2024), Accepted.
カテゴリー: Development of CAE Method Using Physical Simulation by Artificial Intelligence
Yamazaki, Y., Harandi, A., Muramatsu, M., Viardin, A., Apel, M., Brepols, T., Reese, S., Rezaei, S., “A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains”, Engineering with Computers, Vol. ??, pp. 1-29, (2024).
Hiraide, K., Oya, Y., Hirayama, K., Endo, K., Muramatsu, M., ” Development of deep learning model for phase separation structure of diblock copolymer based on self-consistent field analysis”, Advanced Composite Materials, Vol. 00, pp. 00 1-14, Accepted (2024).
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).
Hiraide, K., Hirayama, K., Endo, K., Muramats, M., “Application of deep learning to inverse design of phase separation structure in polymer alloy”, Computational Materials Science, (2021), Accepted.
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).