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Issue title: Deep learning for analysis and synthesis in electromagnetics
Guest editors: Maria Evelina Mognaschi
Article type: Research Article
Authors: Mušeljić, Eniza; | Roppert, Klausa | Domenig, Lukas Daniela | Köstinger, Alice Reinbachera | Kaltenbacher, Manfreda
Affiliations: [a] Institute of Fundamentals and Theory in Electrical Engineering, Graz University of Technology, Austria
Correspondence: [*] Corresponding author: Eniz Mušeljić, Institute of Fundamentals and Theory in Electrical Engineering, Graz University of Technology, Austria. E-mail: [email protected]
Abstract: This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified on synthetic as well as measurement data.
Keywords: Optimization; parameter identification; hysteresis; machine learning; neural networks
DOI: 10.3233/JAE-230107
Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 415-427, 2023
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