Deep Learning-Based Meta-Modeling for Multi-Objective Technology Optimization of Electrical Machines
Author:
Affiliation:
1. Computational Electromagnetics Group, Technische Universität Darmstadt, Darmstadt, Germany
2. Powertrain Solutions, Mechanical Engineering and Reliability, Robert Bosch GmbH, Stuttgart, Germany
Funder
Robert Bosch GmbH and the SFB TRR 361 CREATOR
German Research Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10226214.pdf?arnumber=10226214
Reference29 articles.
1. Multi-Attribute Machine Learning Model for Electrical Motors Performance Prediction
2. Electric Machine Two-dimensional Flux Map Prediction with Ensemble Learning
3. Fast Topology Optimization for PM Motors Using Variational Autoencoder and Neural Networks With Dropout
4. Automatic Design System With Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor
5. Torque Estimation and Control of PMSM Based on Deep Learning
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