Multi-Objective Motor Design Optimization with Physics-Assisted Neural Network Model-Reference-Cited by-同舟云学术

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Multi-Objective Motor Design Optimization with Physics-Assisted Neural Network Model

Published:2023-05-15 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Electric Machines & Drives Conference (IEMDC)
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Author:

Sakamoto Yusuke¹,Xu Yihao²,Wang Bingnan¹,Yamamoto Tatsuya³,Nishimura Yuki³

Affiliation:

1. Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA,02139

2. Northeastern University,Mechanical and Industrial Engineering,Boston,MA,USA,02120

3. Mitsubishi Electric Corporation,Advanced Technology R&D Center,Amagasaki,Japan,661-8661

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10238840/10238781/10238886.pdf?arnumber=10238886

Reference16 articles.

1. Fast and Systematic Design Optimization of Surface-Mounted PM Machines Using Advanced Analytical Models and Subharmonic Elimination Methods

2. Analytical prediction of electromagnetic performance of surface-mounted PM machines based on subdomain model accounting for tooth-tips

3. Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core

4. Analytical Modeling and Design Optimization of a Vernier Permanent Magnet Motor

5. General Formulation of the Electromagnetic Field Distribution in Machines and Devices Using Fourier Analysis

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-Efficient Machine Learning Methods for Electric Motor Surrogate Models;2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC);2024-06-02

2. Electric Motor Surrogate Model Combining Subdomain Method and Neural Network;2023 24th International Conference on the Computation of Electromagnetic Fields (COMPUMAG);2023-05-22

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