Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons
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 constructio...
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2024-10-01
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| author | Silvia Licciardi Guido Ala Elisa Francomano Fabio Viola Michele Lo Giudice Alessandro Salvini Fausto Sargeni Vittorio Bertolini Andrea Di Schino Antonio Faba |
| author_facet | Silvia Licciardi Guido Ala Elisa Francomano Fabio Viola Michele Lo Giudice Alessandro Salvini Fausto Sargeni Vittorio Bertolini Andrea Di Schino Antonio Faba |
| author_sort | Silvia Licciardi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-719b66f0344242f39b866e811da45ac9 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-719b66f0344242f39b866e811da45ac92024-11-08T14:37:39ZengMDPI AGMathematics2227-73902024-10-011221336310.3390/math12213363Neural Network Architectures and Magnetic Hysteresis: Overview and ComparisonsSilvia Licciardi0Guido Ala1Elisa Francomano2Fabio Viola3Michele Lo Giudice4Alessandro Salvini5Fausto Sargeni6Vittorio Bertolini7Andrea Di Schino8Antonio Faba9Department of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, ItalyDepartment of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, ItalyDepartment of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, ItalyDepartment of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, ItalyDepartment of Civil, Computer Science and Aeronautical Technologies Engineering, University of Rome Tre, Via Vito Volterra 62, 00146 Rome, ItalyDepartment of Civil, Computer Science and Aeronautical Technologies Engineering, University of Rome Tre, Via Vito Volterra 62, 00146 Rome, ItalyDepartment of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, ItalyDepartment of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, ItalyDepartment of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, ItalyThe 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.https://www.mdpi.com/2227-7390/12/21/3363deep learningLSTM architectureshybrid neural networks architecturesmagnetic hysteresisPreisach modelnumerical methods |
| spellingShingle | Silvia Licciardi Guido Ala Elisa Francomano Fabio Viola Michele Lo Giudice Alessandro Salvini Fausto Sargeni Vittorio Bertolini Andrea Di Schino Antonio Faba Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons Mathematics deep learning LSTM architectures hybrid neural networks architectures magnetic hysteresis Preisach model numerical methods |
| title | Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons |
| title_full | Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons |
| title_fullStr | Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons |
| title_full_unstemmed | Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons |
| title_short | Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons |
| title_sort | neural network architectures and magnetic hysteresis overview and comparisons |
| topic | deep learning LSTM architectures hybrid neural networks architectures magnetic hysteresis Preisach model numerical methods |
| url | https://www.mdpi.com/2227-7390/12/21/3363 |
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