Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
Session
OC2: AI and machine learning technologies
Time:
Thursday, 25/May/2023:
3:40pm - 5:20pm

Session Chair: Prof. Oszkar Biro , Budapest University of Technology and Economics, Hungary
Session Chair: Prof. Zhuoxiang Ren , Sorbonne University, France


Presentations
3:40pm - 4:00pm
ID: 359 / OC2: 1
Topics: Static and Quasi-Static Fields, AI and Machine Learning Technologies
Keywords: Deep learning, surrogate model, finite element analysis, magneto-thermal analysis, TEAM Problem

Predicting Transient Thermal Maps via a DNN Method for Solving TEAM Workshop Problem 36

Paolo Di Barba 1 , Maria Evelina Mognaschi 1 , Anna Maria Cavazzini 2 , Matteo Ciofani 2 , Fabrizio Dughiero 2 , Michele Forzan 2 , Matteo Lazzarin 2 , David Alister Lowther 3 , Jan K. Sykulski 4

1 University of Pavia, Italy; 2 University of Padova, Italy; 3 McGill University, Montreal, Canada; 4 University of Southampton, Southampton, UK



4:00pm - 4:20pm
ID: 449 / OC2: 2
Topics: Optimization and Design, AI and Machine Learning Technologies
Keywords: Interior Permanent Magnet Synchronous Motor, Shapley Additive Explanations, Topology optimization

Topology Optimization of Permanent Magnet Synchronous Motor with Shapley Additive Explanations

Hidenori Sasaki , Koichi Yamamura

Hosei University, Japan




4:20pm - 4:40pm
ID: 379 / OC2: 3
Topics: AI and Machine Learning Technologies
Keywords: Convolutional neural network, design optimization, iron loss, permanent magnet motor, transformer.

Investigation of Iron Loss Prediction Model for Automatic Design System of IPMSMs

Yuki Shimizu

Ritsumeikan University, Japan




4:40pm - 5:00pm
ID: 424 / OC2: 4
Topics: Material Modelling
Keywords: Magnetic Hysteresis, Magnetic Materials, Neural Networks, Deep Learning

The Application of Neural Networks to the Computation of Magnetic Hysteresis

Niilo Vuokila, Christos Cunning, Jayson Zhang, Nader Akel, Arbaaz Khan, David Lowther

McGill University, Canada




5:00pm - 5:20pm
ID: 300 / OC2: 5
Topics: Optimization and Design, Novel Computational Methods for Machines and Devices, AI and Machine Learning Technologies
Keywords: Deep Learning, Surrogate Model, Finite Element Analysis, Electrical Motors

Comparison of Learning-based Surrogate Models for Electric Motors

Yihao Xu 1,2 , Bingnan Wang 1 , Yusuke Sakamoto 1,3 , Tatsuya Yamamoto 3 , Yuki Nishimura 3

1 Mitsubishi Electric Research Laboratories, United States of America; 2 Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02120 USA; 3 Advanced Technology R&D Center, Mitsubishi Electric Corporation, Amagasaki, Hyogo 661-8661 Japan