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This short paper explores the magnetization reversal process in non-oriented electromagnetic steel sheets using an extended Landau free energy model. The approach leverages magnetic domain analysis and modeling to deepen understanding of magnetization dynamics in soft magnetic materials.

Recommended citation: Taniwaki, M., Foggiatto, A. L., Mitsumata, C., Yamazaki, T., Obayashi, I., Hiraoka, Y., Igarashi, Y., Mizutori, Y., Sepehri-Amin, H., Ohkubo, T., & Kotsugi, M. (2023). "Analysis of Magnetization Reversal Process of Non-Oriented Electromagnetic Steel Sheet by Extended Landau Free Energy Model." In *2023 IEEE International Magnetic Conference – INTERMAG Short Papers*, pp. 1–2. https://doi.org/10.1109/INTERMAGShortPapers58606.2023.10228817
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Visualization of the Magnetostriction Mechanism in Fe-Ga Alloy Single Crystal Using Dimensionality Reduction Techniques

Published in IEEE Transactions on Magnetics, Vol. 59, No. 11, pp. 1–4, 2023

This study presents a machine-learning-aided analysis of magnetostriction mechanisms in Fe-Ga alloy single crystals. Using dimensionality reduction techniques, the research provides a visual and interpretable framework for understanding magnetization and domain behavior at the microscopic scale.

Recommended citation: Foggiatto, A. L., Mizutori, Y., Yamazaki, T., Sato, S., Masuzawa, K., Nagaoka, R., Taniwaki, M., Fujieda, S., Suzuki, S., Ishiyama, K., Fukuda, T., Igarashi, Y., Mitsumata, C., & Kotsugi, M. (2023). "Visualization of the Magnetostriction Mechanism in Fe-Ga Alloy Single Crystal Using Dimensionality Reduction Techniques." *IEEE Transactions on Magnetics*, 59(11), 1–4. https://doi.org/10.1109/TMAG.2023.3312372
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Analysis of the High-Frequency Magnetization Process Through Machine Learning and Topological Data Techniques

Published in 2024 IEEE International Magnetic Conference – INTERMAG Short Papers, 2024

This short paper presents an interpretable machine learning and topological data analysis (TDA) approach for analyzing magnetization dynamics under high-frequency excitation. The study highlights the potential of data-driven modeling in understanding microstructural influences on magnetic 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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Analysis of the Excess Loss in High-Frequency Magnetization Process Through Machine Learning and Topological Data Analysis

Published in IEEE Transactions on Magnetics, Vol. 60, No. 9, pp. 1–5, 2024

This paper investigates high-frequency magnetic losses using machine learning and topological data analysis (TDA). By analyzing spin dynamics in non-oriented electrical steel, the study reveals how excess loss components can be understood and predicted via interpretable ML models and persistent homology.

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 Excess Loss in High-Frequency Magnetization Process Through Machine Learning and Topological Data Analysis." *IEEE Transactions on Magnetics*, 60(9), 1–5. https://doi.org/10.1109/TMAG.2024.3406717
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Quantification of the Coercivity Factor in Soft Magnetic Materials at Different Frequencies Using Topological Data Analysis

Published in IEEE Transactions on Magnetics, Vol. 60, No. 9, pp. 1–5, 2024

This study introduces a method to quantify the coercivity factor in soft magnetic materials across different frequencies using topological data analysis (TDA). By analyzing magnetic domain structures via persistent homology, it offers a new path for understanding frequency-dependent magnetic properties.

Recommended citation: Nagaoka, R., Masuzawa, K., Taniwaki, M., Foggiatto, A. L., Yamazaki, T., Obayashi, I., Hiraoka, Y., Mitsumata, C., & Kotsugi, M. (2024). "Quantification of the Coercivity Factor in Soft Magnetic Materials at Different Frequencies Using Topological Data Analysis." *IEEE Transactions on Magnetics*, 60(9), 1–5. https://doi.org/10.1109/TMAG.2024.3408002
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Automated Identification of the Origin of Energy Loss in Non-Oriented Electrical Steel by Feature-Extended Ginzburg–Landau Free-Energy Framework

Published in Research Square (Preprint), 2024

This study introduces an extended Ginzburg–Landau model with extended features to identify the physical origin of energy loss in non-oriented electrical steel. The interpretable, automated framework demonstrates potential for smart materials analysis in industrial settings.

Recommended citation: Taniwaki, M., Nagaoka, R., Masuzawa, K., Sato, S., Foggiatto, A. L., Mitsumata, C., Yamazaki, T., Obayashi, I., Hiraoka, Y., Igarashi, Y., Mizutori, Y., Sepehri-Amin, H., Ohkubo, T., Mogi, H., & Kotsugi, M. (2024). "Automated Identification of the Origin of Energy Loss in Non-Oriented Electrical Steel by Feature-Extended Ginzburg–Landau Free-Energy Framework." Research Square. https://doi.org/10.21203/rs.3.rs-5383617/v1
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Automated Identification of the Origin of Energy Loss in Non-Oriented Electrical Steel by Feature-Extended Ginzburg–Landau Free-Energy Framework

Published in Scientific Reports (Nature Portfolio), 2025

Recommended citation: Taniwaki, M., Nagaoka, R., Masuzawa, K., Sato, S., Foggiatto, A. L., Mitsumata, C., Yamazaki, T., Obayashi, I., Hiraoka, Y., Igarashi, Y., Mizutori, Y., Sepehri-Amin, H., Ohkubo, T., Mogi, H., & Kotsugi, M. (2025). "Automated Identification of the Origin of Energy Loss in Non-Oriented Electrical Steel by Feature-Extended Ginzburg–Landau Free-Energy Framework." Scientific Reports, 15, 357. https://doi.org/10.1038/s41598-025-00357-z
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Persistent Homology-Based Descriptor of Topological Ordering in Two-Dimensional Quasi-Particle Systems with Application to Skyrmion Lattices

Published in arXiv:2504.14688 [cond-mat.stat-mech], 2025

This paper proposes a persistent homology-based descriptor to characterize topological ordering in 2D quasi-particle systems. The descriptor efficiently identifies phase transitions and is demonstrated on skyrmion lattices, with potential for broader applications.

Recommended citation: Taniwaki, M., Winkler, T. B., Rothörl, J., Gruber, R., Mitsumata, C., Kotsugi, M., & Kläui, M. (2025). "Persistent Homology-Based Descriptor of Topological Ordering in Two-Dimensional Quasi-Particle Systems with Application to Skyrmion Lattices." arXiv:2504.14688.
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