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  4. Magnetic properties identification by using a bi-objective optimal multi-fidelity neural network
 
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2021
Journal Article
Title

Magnetic properties identification by using a bi-objective optimal multi-fidelity neural network

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 (INN CJ ) 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 (INN TR ) and a single-fidelity inverse neural network (INN SF ). Finally, a real B - H curve identification task has been solved, thereby validating the conjugate inverse net.
Author(s)
Baldan, Marco  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Barba, Paolo di
Department of Electrical, Computer and Biomedical Engineering, University of Pavia
Nacke, Berard
Institute of Electrotechnology, Leibniz University Hannover
Journal
IEEE transactions on magnetics  
DOI
10.1109/TMAG.2021.3068705
Language
English
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
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