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 preprint presents an interpretable, physics-informed machine learning framework based on a feature-extended Ginzburg–Landau free-energy model for analyzing energy loss mechanisms in non-oriented electrical steel.
Using a combination of simulation data and persistent topological features, the study offers a new method to automatically identify the origin of loss components, with relevance to industrial material development and magnetic systems research.
Authors & Affiliations:
- Michiki Taniwaki, Ryunosuke Nagaoka, Ken Masuzawa, Shunsuke Sato, Alexandre Lira Foggiatto, Chiharu Mitsumata, Takahiro Yamazaki — Tokyo University of Science
- Ippei Obayashi — Okayama University
- Yasuaki Hiraoka — Kyoto University
- Yasuhiko Igarashi, Yuta Mizutori — Tsukuba University
- Hossein Sepehri-Amin — National Institute for Materials Science (NIMS)
- Tadakatsu Ohkubo — National Institute for Materials Science (NIMS)
- Hisashi Mogi — Nippon Steel
- Masato Kotsugi — Tokyo University of Science
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
Download Paper