Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

Silvia Licciardi (), Guido Ala, Elisa Francomano, Fabio Viola, Michele Lo Giudice, Alessandro Salvini, Fausto Sargeni, Vittorio Bertolini, Andrea Di Schino and Antonio Faba
Additional contact information
Silvia Licciardi: Department of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Guido Ala: Department of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Elisa Francomano: Department of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Fabio Viola: Department of Electrical Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Michele Lo Giudice: Department of Civil, Computer Science and Aeronautical Technologies Engineering, University of Rome Tre, Via Vito Volterra 62, 00146 Rome, Italy
Alessandro Salvini: Department of Civil, Computer Science and Aeronautical Technologies Engineering, University of Rome Tre, Via Vito Volterra 62, 00146 Rome, Italy
Fausto Sargeni: Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy
Vittorio Bertolini: Department of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, Italy
Andrea Di Schino: Department of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, Italy
Antonio Faba: Department of Engineering, University of Perugia, Via G. Duranti 93, 06123 Perugia, Italy

Mathematics, 2024, vol. 12, issue 21, 1-23

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.

Keywords: deep learning; LSTM architectures; hybrid neural networks architectures; magnetic hysteresis; Preisach model; numerical methods; global optimization; gradient methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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