Details
| Original language | English |
|---|---|
| Journal | IEEE transactions on magnetics |
| Volume | 57 |
| Issue number | 6 |
| Early online date | 24 Mar 2021 |
| Publication status | Published - 17 May 2021 |
Abstract
In order to identify the magnetic properties of magnetic steel, the synergy between the data arising from the experimental activity, an FE model, and the use of a multi-fidelity surrogate could relieve the burden of the total cost. A neural network, with as many outputs as fidelity levels, is adopted in quality of metamodel to describe the forward problem [forward neural network (FNN)]. FNN is trained using multiple losses aiming at getting a robust surrogate that is poorly sensitive to the chosen norm. This makes it bi-objective optimal since several error metrics are simultaneously minimized. In addition, a conjugate, inverse net (INNCJ) is built, which is a ready-to-use tool for inverse properties identification, since no optimization runs are required. Its performances are compared to those obtained with a transfer learning-based approach (INNTR) and a single-fidelity inverse neural network (INNSF). Finally, a real $B - H$ curve identification task has been solved, thereby validating the conjugate inverse net.
Keywords
- Bi-objective optimality, inverse problem, magnetic permeability, multi-fidelity, neural network
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Engineering(all)
- Electrical and Electronic Engineering
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In: IEEE transactions on magnetics, Vol. 57, No. 6, 17.05.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Magnetic Properties Identification by Using a Bi-Objective Optimal Multi-Fidelity Neural Network
AU - Baldan, Marco
AU - Barba, Paolo Di
AU - Nacke, Bernard
PY - 2021/5/17
Y1 - 2021/5/17
N2 - In order to identify the magnetic properties of magnetic steel, the synergy between the data arising from the experimental activity, an FE model, and the use of a multi-fidelity surrogate could relieve the burden of the total cost. A neural network, with as many outputs as fidelity levels, is adopted in quality of metamodel to describe the forward problem [forward neural network (FNN)]. FNN is trained using multiple losses aiming at getting a robust surrogate that is poorly sensitive to the chosen norm. This makes it bi-objective optimal since several error metrics are simultaneously minimized. In addition, a conjugate, inverse net (INNCJ) is built, which is a ready-to-use tool for inverse properties identification, since no optimization runs are required. Its performances are compared to those obtained with a transfer learning-based approach (INNTR) and a single-fidelity inverse neural network (INNSF). Finally, a real $B - H$ curve identification task has been solved, thereby validating the conjugate inverse net.
AB - In order to identify the magnetic properties of magnetic steel, the synergy between the data arising from the experimental activity, an FE model, and the use of a multi-fidelity surrogate could relieve the burden of the total cost. A neural network, with as many outputs as fidelity levels, is adopted in quality of metamodel to describe the forward problem [forward neural network (FNN)]. FNN is trained using multiple losses aiming at getting a robust surrogate that is poorly sensitive to the chosen norm. This makes it bi-objective optimal since several error metrics are simultaneously minimized. In addition, a conjugate, inverse net (INNCJ) is built, which is a ready-to-use tool for inverse properties identification, since no optimization runs are required. Its performances are compared to those obtained with a transfer learning-based approach (INNTR) and a single-fidelity inverse neural network (INNSF). Finally, a real $B - H$ curve identification task has been solved, thereby validating the conjugate inverse net.
KW - Bi-objective optimality
KW - inverse problem
KW - magnetic permeability
KW - multi-fidelity
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85103254054&partnerID=8YFLogxK
U2 - 10.1109/TMAG.2021.3068705
DO - 10.1109/TMAG.2021.3068705
M3 - Article
AN - SCOPUS:85103254054
VL - 57
JO - IEEE transactions on magnetics
JF - IEEE transactions on magnetics
SN - 0018-9464
IS - 6
ER -