1. Improvement of CNN-Based Anisotropic Magnetostatic Field Computation via Adaptive Data Subset Selection
- Author
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Ruohan Gong, Zuqi Tang, L2EP - Équipe Outils et Méthodes Numériques (OMN), Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 (L2EP), Centrale Lille-Université de Lille-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Arts et Métiers Sciences et Technologies, Université catholique de Lille (UCL)-Université catholique de Lille (UCL), and ANR-20-CE42-0009,WISSTITWIN,Devloppement de technologies miniaturisées de capteurs SAW multiparamétres pour l'implementation du jumeau numérique des machines électriques.(2020)
- Subjects
[PHYS]Physics [physics] ,[SPI]Engineering Sciences [physics] ,Electrical and Electronic Engineering ,Electronic, Optical and Magnetic Materials - Abstract
International audience; A numerical issue arises when we extend the convolutional neural network (CNN) U-net to the anisotropic magnetostatic field computation. The output magnetic field has a significant gradient with respect to the input geometry parameter, which introduces inevitable errors in the training process to degrade the performance of deep learning (DL). To address this issue, the subset selection approach is utilized to divide the whole database into several subsets, where the samples are assigned according to the gradient between the input and output. Then these subsets with different sample densities are combined into a global one. Taking the uniform dataset with the same sample size as a comparison, the influence of subset selection on DL is investigated by comparing the performance of CNN on different datasets. Numerical experiments illustrate that the adaptive subset selection can be employed to improve the accuracy and efficiency of the CNN network.
- Published
- 2022
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