Fast Topology Optimization for PM Motors Using Variational Autoencoder and Neural Networks With Dropout-Reference-Cited by-同舟云学术

Fast Topology Optimization for PM Motors Using Variational Autoencoder and Neural Networks With Dropout

Published:2023-05 Issue:5 Volume:59 Page:1-4
ISSN:0018-9464
Container-title:IEEE Transactions on Magnetics
language:
Short-container-title:IEEE Trans. Magn.

Author:

Sato Hayaho¹^ORCID,Igarashi Hajime¹^ORCID

Affiliation:

1. Graduate School of Information Science and Technology, Hokkaido University, Hokkaido, Japan

Funder

Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation

Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Doctoral Program for Data-Related InnoVation Expert Hokkaido University (D-DRIVE-HU) Program

Japan Society for the Promotion of Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/20/10108398/10036443.pdf?arnumber=10036443

Reference10 articles.

1. Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization

2. Auto-encoding variational Bayes;kingma;Proc ICLR,2014

3. A fast and elitist multiobjective genetic algorithm: NSGA-II

4. Variational Autoencoder-Based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines

5. Automatic design system with generative adversarial network and convolutional neural network for optimization design of interior permanent magnet synchronous motor;shimizu;IEEE Trans Energy Convers,2022

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