Cite
[1]
S. Licciardi et al., ‘Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons’, Mathematics, vol. 12, no. 21, p. 3363, Oct. 2024, doi: 10.3390/math12213363.
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FirstAuthor:: Licciardi, Silvia
Author:: Ala, Guido
Author:: Francomano, Elisa
Author:: Viola, Fabio
Author:: Lo Giudice, Michele
Author:: Salvini, Alessandro
Author:: Sargeni, Fausto
Author:: Bertolini, Vittorio
Author:: Di Schino, Andrea
Author:: Faba, Antonio
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Title:: Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons
Year:: 2024
Citekey:: licciardiNeuralNetworkArchitectures2024
itemType:: journalArticle
Journal:: Mathematics
Volume:: 12
Issue:: 21
Pages:: 3363
DOI:: 10.3390/math12213363
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 electrical equipment. In particular, the 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 the deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. The intent of this paper is to provide the features of a wide range of deep learning tools to be applied to magnetic hysteresis context and beyond. The possibilities of building neural networks in hybrid form are innumerable, so it is not plausible to illustrate them in a single paper, but in the present context, several neural networks used in the 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 modeling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for the analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices. .
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