Autors: Mateev, V. M., Marinova, I. Y.
Title: Machine Learning in Magnetic Field Calculations
Keywords: Bench-mark problems, Dirichlet boundary, Distributed excitat

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


  1. Turner, L. (1985) Electromagnetic Effects in Magnetics, November
  2. Wiatowski, T., Bolcskei, H. A, 2018, Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction, IEEE Transactions on Information Theory, Volume 64(3), pp. pp. 1845-1866
  3. Yang, H.-F., Lin, K., Chen, C.-S., 2018, Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40(2), pp. pp. 437-451, art. no. 7849132
  4. Ansys Inc, 2018, Testing Electromagnetic Analysis Methods (T. E. A. M.) International Compumag Society, Problems List, Ansys Inc, <>, Дата на последен преглед (Last accessed on): 17.11.2020
  5. (2015) Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis-Microsoft Research research. microsoft. com
  6. Deng, L., Yu, D., 2013, Deep learning: Methods and applications, Foundations and Trends in Signal Processing, Volume 7(3-4), pp. pp. 197-387


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
3. TaiNguyen, V.,Bollmann, S., Bermingham, M. and Dargusch, M.S., 2022. Efficient Modelling of Permanent Magnet Field Distribution for Deep Learning Applications. Journal of Magnetism and Magnetic Materials, Volume 559, p.169521. - 2022 - в издания, индексирани в Scopus или Web of Science
4. Abhishek Talapatra, Udaykumar Gajera, Syam Prasad P, Jeyaramane Arout Chelvane, and Jyoti Ranjan Mohanty, Understanding the Magnetic Microstructure through Experiments and Machine Learning Algorithms, ACS Applied Materials & Interfaces 2022 14 (44), 50318-50330, DOI: 10.1021/acsami.2c12848 - 2022 - в издания, индексирани в Scopus или Web of Science
5. Valencia, F., Arcos, H., Quilumba, F. (2022). Mechanical Stress in Power Transformer Winding Conductors: A Support Vector Regression Approach. In: Botto-Tobar, M., Zambrano Vizuete, M., Diaz Cadena, A., Vizuete, A.Z. (eds) Latest Advances in Electrical Engineering, and Electronics. Lecture Notes in Electrical Engineering, vol 933. Springer, Cham. - 2022 - в издания, индексирани в Scopus или Web of Science
6. Q. Peng et al., "Magnetic Field Simulation of Reactor Based on Deep Neural Networks," in IEEE Transactions on Power Delivery, vol. 38, no. 3, pp. 2224-2227, June 2023, doi: 10.1109/TPWRD.2023.3256122. - 2023 - в издания, индексирани в Scopus или Web of Science
7. Van Tai Nguyen Steffen Bollmann Michael Bermingham Ha Xuan Nguyen Matthew S. Dargusch , "Deep Learning Based Modelling of Three-Dimensional Magnetic Field," Progress In Electromagnetics Research B, Vol. 100, 173-189, 2023. - 2023 - в издания, индексирани в Scopus или Web of Science

Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus