Integration of Experiment and Numerical Analysis
We are developing novel evaluation methods using analytical techniques such as machine learning on experimental data to elucidate phenomena. Additionally, we are developing data assimilation analysis methods aimed at estimating numerical parameters for numerical simulations that reproduce experiments.
References
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Tanaka, M., Sasaki, K., Punyafu, J., Muramatsu, M., Murayama, M., “Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel”, Scientific Reports, Vol. 15, No. 14435, pp. 1-10, (2025).
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Murata, H., Ihara, S., Endo, K. and Muramatsu, M., “Data Assimilation Based on the Ensemble Kalman Filter for Dislocation Motion Using Dislocation Dynamics Simulation”, Modelling and Simulation in Materials Science and Engineering, Vol. 42, 15, (2026).
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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).