Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization
Author:
Affiliation:
1. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
2. Advanced Technology Research and Development Center, Mitsubishi Electric Corporation, Amagasaki, Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
Link
http://xplorestaging.ieee.org/ielx7/20/9868170/09756554.pdf?arnumber=9756554
Reference8 articles.
1. Topology Optimization Accelerated by Deep Learning
2. Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology
3. Deep learning-based surrogate model for fast multi-material topology optimization of IPM Motor;igarashi;COMPEL Int J Comput Math Elect Electron Eng,2022
4. A High-Fidelity and Computationally Efficient Model for Interior Permanent-Magnet Machines Considering the Magnetic Saturation, Spatial Harmonics, and Iron Loss Effect
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