1. Deep Learning for Magnetic Field Estimation.
- Author
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Khan, Arbaaz, Ghorbanian, Vahid, and Lowther, David
- Subjects
DEEP learning ,MAGNETIC fields ,MAXWELL equations ,PERMANENT magnet motors ,SUPERVISED learning ,ARTIFICIAL neural networks - Abstract
This paper investigates the feasibility of novel data-driven deep learning (DL) models to predict the solution of Maxwell’s equations for low-frequency electromagnetic (EM) devices. With ground truth (empirical evidence) data being generated from a finite-element analysis solver, a deep convolutional neural network is trained in a supervised manner to learn a mapping for magnetic field distribution for topologies of different complexities of geometry, material, and excitation, including a simple coil, a transformer, and a permanent magnet motor. Preliminary experiments show DL model predictions in close agreement with the ground truth. A probabilistic model is introduced to improve the accuracy and to quantify the uncertainty in the prediction, based on Monte Carlo dropout. This paper establishes a basis for a fast and generalizable data-driven model used in the analysis, design, and optimization of EM devices. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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