Version 1
: Received: 26 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (05:50:43 CEST)
How to cite:
Licciardi, S.; Ala, G.; Francomano, E.; Viola, F.; Lo Giudice, M.; Salvini, A.; Sargeni, F.; Bonaiuto, V.; Di Schino, A.; Faba, A. Neural Network Architectures and Magnetic Hysteresis - Overview and Comparisons. Preprints2024, 2024092246. https://doi.org/10.20944/preprints202409.2246.v1
Licciardi, S.; Ala, G.; Francomano, E.; Viola, F.; Lo Giudice, M.; Salvini, A.; Sargeni, F.; Bonaiuto, V.; Di Schino, A.; Faba, A. Neural Network Architectures and Magnetic Hysteresis - Overview and Comparisons. Preprints 2024, 2024092246. https://doi.org/10.20944/preprints202409.2246.v1
Licciardi, S.; Ala, G.; Francomano, E.; Viola, F.; Lo Giudice, M.; Salvini, A.; Sargeni, F.; Bonaiuto, V.; Di Schino, A.; Faba, A. Neural Network Architectures and Magnetic Hysteresis - Overview and Comparisons. Preprints2024, 2024092246. https://doi.org/10.20944/preprints202409.2246.v1
APA Style
Licciardi, S., Ala, G., Francomano, E., Viola, F., Lo Giudice, M., Salvini, A., Sargeni, F., Bonaiuto, V., Di Schino, A., & Faba, A. (2024). Neural Network Architectures and Magnetic Hysteresis - Overview and Comparisons. Preprints. https://doi.org/10.20944/preprints202409.2246.v1
Chicago/Turabian Style
Licciardi, S., Andrea Di Schino and Antonio Faba. 2024 "Neural Network Architectures and Magnetic Hysteresis - Overview and Comparisons" Preprints. https://doi.org/10.20944/preprints202409.2246.v1
Abstract
The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of the electrical equipment. In particular, modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of Deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. Particularly in the paper, different Neural networks used in scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modelling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.
Keywords
Deep Learning 68T07; LSTM architectures; hybrid neural networks architectures 68T99; magnetic hysteresis 78A25; Preisach model 78A99; numerical methods, global optimization 65K05; gradient methods 90C52
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
