Autors: Mateev, V. M., Marinova, I. Y.
Title: Machine Learning in Magnetic Field Calculations
Keywords: Bench-mark problems, Dirichlet boundary, Distributed excitation, Finite element method models, Harmonic magnetic fieldMachine learning approaches, Magnetic field calculations, Non-homogeneous

Abstract: Here is presented a machine learning approach for 2D steady-state and harmonic magnetic field calculations based on Poisson and Helmholtz equations for Dirichlet boundary problems. The approach is implemented on multilayer convolutional neural network trained over the Compumag 1b TEAM. benchmark problem variations dataset. Implementation is suitable for non-homogeneous magnetic properties domains and distributed excitation sources. Results accuracy is estimated in comparison with Finite Element Method model of the same problem

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Issue

19th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering, ISEF 2019, vol. ISEF 2019, issue 19, pp. Article number 9096969, 2019, France, IEEE Inc, DOI 10.1109/ISEF45929.2019.9096969

Copyright IEEE

Цитирания (Citation/s):
1. DOI: 10.1088/1742-6596/1624/5/052002 - 2020 - в издания, индексирани в Scopus или Web of Science
2. Liu, P., Zhang, Z., Meng, Z., Gao, N., Monocular depth estimation with joint attention feature distillation and wavelet-based loss function, (2021) Sensors (Switzerland), 21 (1), art. no. 54, pp. 1-21. DOI: 10.3390/s21010054 - 2021 - в издания, индексирани в Scopus или Web of Science
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Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus