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