Analysis of the High-Frequency Magnetization Process Through Machine Learning and Topological Data Techniques

Authors & Affiliations:

• Alexandre Lira Foggiatto, Ryunosuke Nagaoka, Michiki Taniwaki, Takahiro Yamazaki, Chiharu Mitsumata, Masato Kotsugi — Tokyo University of Science
• Takeshi Ogasawara — National Institute of Advanced Industrial Science and Technology (AIST), Japan
• Ippei Obayashi — Okayama University
• Yasuaki Hiraoka — Kyoto University

This short paper introduces an interpretable ML framework enhanced by topological data techniques, aimed at evaluating high-frequency magnetization behavior. By examining local magnetic configurations and leveraging persistent homology, the study reveals novel correlations between domain dynamics and material loss behavior.

Recommended citation: Foggiatto, A. L., Nagaoka, R., Taniwaki, M., Yamazaki, T., Ogasawara, T., Obayashi, I., Hiraoka, Y., Mitsumata, C., & Kotsugi, M. (2024). "Analysis of the High-Frequency Magnetization Process Through Machine Learning and Topological Data Techniques." In *2024 IEEE International Magnetic Conference – INTERMAG Short Papers*, pp. 1–2. https://doi.org/10.1109/INTERMAGShortPapers61879.2024.10576971
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