ID: 438
/ PB-M2: 1
Topics:
Optimization and Design, AI and Machine Learning Technologies
Keywords:
Convolutional Neural Network, Deep Learning, Interior Permanent Magnet motor, Topology Optimization
Prediction of Interior Permanent Magnet Motor Characteristics Using CNN with Vector Input of Magnetic Flux Density Distribution
Kazuhisa Iwata
1
, Hidenori Sasaki
1
, Hajime Igarashi
2
, Daisuke Nakagawa
3
, Tomoya Ueda
3
1
Hosei University, Japan;
2
Hokkaido University, Japan;
3
Nidec Research and Development Center, Japan
ID: 396
/ PB-M2: 2
Topics:
Numerical Techniques, AI and Machine Learning Technologies
Keywords:
Finite Element Analysis, Eddy Currents, Graph Neural Networks, Approximation Error
Discretization Error Approximation for FEM-Based Eddy Current Models using Neural Networks
Moritz von Tresckow
, Herbert De Gersem, Dimitrios Loukrezis
TU Darmstadt, Germany
ID: 356
/ PB-M2: 3
Topics:
AI and Machine Learning Technologies
Keywords:
Neural Networks, Direct and Inverse Electromagnetic problems
Physics-Informed Neural Networks for the Resolution of Analysis Problems in Electromagnetics
Sami Barmada
1
, Paolo Di Barba
2
,
Alessandro Formisano
3
, Maria Evelina Mognaschi
2
, Mauro Tucci
1
1
Universita' di Pisa;
2
Universita' di Pavia;
3
Universita' della Campania Luigi Vanvitelli, Italy
ID: 381
/ PB-M2: 4
Topics:
AI and Machine Learning Technologies
Keywords:
Finite element method, neural network, partial difference equation, physics-informed neural network
A Fast Physics-informed Neural Network Based on Extreme Learning Machine for Solving Magnetostatic Problems
Takahiro Sato
1
, Hidenori Sasaki
2
, Yuki Sato
3
1
Muroran Institute of Technology, Japan;
2
Faculty of Science and Engineering, Hosei University, Japan;
3
Department of Electrical Engineering and Electronics, Aoyama Gakuin University, Japan
ID: 285
/ PB-M2: 5
Topics:
Mathematical Modelling and Formulations, Numerical Techniques, Electromagnetic Sensors, Sensing and Metrology, AI and Machine Learning Technologies
Keywords:
Boundary conditions; Capacitor; inverse problems; deep learning; numerical analysis
Boundary-Decoder network for inverse prediction of capacitor electrostatic analysis
Kart L Lim
, RAHUL DUTTA, MIHAI ROTARU
Institute of Microelectronics, Singapore
ID: 211
/ PB-M2: 6
Topics:
Optimization and Design, AI and Machine Learning Technologies
Keywords:
Genetic algorithms, Convolutional neural networks, Permanent magnet machines, Finite element methods
1DCNN as an Approximation Model for Torque Optimization of Spoke Type Electrical Machines
Marcelo D. Silva
, Sandra Eriksson
Department of Electrical Engineering, Uppsala University, Sweden
ID: 544
/ PB-M2: 7
Topics:
AI and Machine Learning Technologies
Keywords:
Deep Learning, Partial Differential Equation, Computational electromagnetics, Magnetic materials
Static Magnetic Field Simulation using Deep Learning-based Method
Katsuhiko Yamaguchi
, Masaharu Matsumoto, Kenji Suzuki
Fukushima university, Japan
ID: 430
/ PB-M2: 8
Topics:
Static and Quasi-Static Fields, AI and Machine Learning Technologies
Keywords:
deep learning, wireless power transmission, optimization
A deep learning approach to the optimization of the transferred power in dynamic WPT systems
Manuele Bertoluzzo
1
, Paolo Di Barba
2
, Michele Forzan
1
,
Maria Evelina Mognaschi
2
, Elisabetta Sieni
3
1
University of Padova, Italy;
2
University of Pavia, Italy;
3
University of Insubria, Varese, Italy
ID: 512
/ PB-M2: 9
Topics:
AI and Machine Learning Technologies
Keywords:
Convolutional neural network, reluctance motors, feature extraction, design optimization
Feature Extraction and Visualization Using Convolutional Neural Networks for Design Optimization of Synchronous Reluctance Motors
Marie Katsurai
, Yasuhito Takahashi
Doshisha University, Japan
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