# A neural approach for the numerical modeling of two-dimensional magnetic hysteresis

## Abstract

This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, making possible a full computer simulation in a reasonable time. The neural system proposed consists of four inputs representing the magnetic field and the magnetic inductions components at each time step and it is trained by 2-d measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the neural system returns the predicted value of the field H at the same time step. A suitable partitioning of the neural system, described in the paper, makes the computing process rather fast. Validations with experimental tests and simulations for non-symmetric and minor loops are presented.

- Authors:

- Dipartimento di Ingegneria, Università di Perugia, Perugia (Italy)
- Dipartimento di Ingegneria, Università di Roma Tre, Roma (Italy)

- Publication Date:

- OSTI Identifier:
- 22410063

- Resource Type:
- Journal Article

- Journal Name:
- Journal of Applied Physics

- Additional Journal Information:
- Journal Volume: 117; Journal Issue: 17; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-8979

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; COMPUTERIZED SIMULATION; HYSTERESIS; INDUCTION; MAGNETIC FIELDS; MAGNETIC MATERIALS; MAXWELL EQUATIONS; NEURAL NETWORKS; NONLINEAR PROBLEMS; SYMMETRY; TWO-DIMENSIONAL SYSTEMS

### Citation Formats

```
Cardelli, E., Faba, A., E-mail: antonio.faba@unipg.it, Laudani, A., Riganti Fulginei, F., and Salvini, A.
```*A neural approach for the numerical modeling of two-dimensional magnetic hysteresis*. United States: N. p., 2015.
Web. doi:10.1063/1.4916306.

```
Cardelli, E., Faba, A., E-mail: antonio.faba@unipg.it, Laudani, A., Riganti Fulginei, F., & Salvini, A.
```*A neural approach for the numerical modeling of two-dimensional magnetic hysteresis*. United States. doi:10.1063/1.4916306.

```
Cardelli, E., Faba, A., E-mail: antonio.faba@unipg.it, Laudani, A., Riganti Fulginei, F., and Salvini, A. Thu .
"A neural approach for the numerical modeling of two-dimensional magnetic hysteresis". United States. doi:10.1063/1.4916306.
```

```
@article{osti_22410063,
```

title = {A neural approach for the numerical modeling of two-dimensional magnetic hysteresis},

author = {Cardelli, E. and Faba, A., E-mail: antonio.faba@unipg.it and Laudani, A. and Riganti Fulginei, F. and Salvini, A.},

abstractNote = {This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, making possible a full computer simulation in a reasonable time. The neural system proposed consists of four inputs representing the magnetic field and the magnetic inductions components at each time step and it is trained by 2-d measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the neural system returns the predicted value of the field H at the same time step. A suitable partitioning of the neural system, described in the paper, makes the computing process rather fast. Validations with experimental tests and simulations for non-symmetric and minor loops are presented.},

doi = {10.1063/1.4916306},

journal = {Journal of Applied Physics},

issn = {0021-8979},

number = 17,

volume = 117,

place = {United States},

year = {2015},

month = {5}

}