AI / Machine Learning
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[JFT198] Rapid Design Exploration Using Surrogate Models
This document provides a sample file of a book with Excel macros that explore designs using surrogate models. This document describes how to use this book with Excel macros to obt…
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[JFT152] Output of Response Values Using Surrogate Models
In this document, a macro is provided for quickly evaluating the results obtained by design parameter combinations using surrogate models. This document, as a workflow for paramet…
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[JFT146] Optimization Calculation Using Surrogate Models
This document describes as the workflow for optimization calculations using surrogate models creating training data, creating the sample case, and using a surrogate model in runni…
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The Future of R&D and Design: What Can Electromagnetic Field Analysis and Artificial Intelligence Do for You?
Hajime Igarashi, Hokkaido University
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Smart Motor Control Technology Evolving with AI – Virtual Sensors and Fault Detection –
Atsushi Katagiri, MathWorks Japan
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[JFT188] Creation and Use of Surrogate Models Using Multiple Analysis Case Results
This document describes the procedures to create a surrogate model using results from an analysis with multiple cases to review response value maps by calling that surrogate model…
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1 : JMAG-Ansys-Optimus Electromagnetic Field-Structure Analysis 2 : AI and PIDO Integration -Fast non-parametric shape optimisation-
Mio Hashiba, CYBERNET SYSTEMS CO., LTD.
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Electrical, Electronics and Information Engineering, Engineering, Nagaoka University of Technology
Value the challenges in developing new technologies, and taking the initiative in motor structure innovations
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Automatic Design System for Permanent Magnet Synchronous Motors Using Deep Generative Model
Yuki Shimizu, Ritsumeikan University
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Calculation Cost Reduction Method in Multi-Objective Optimization Using Machine Learning: Case Study on Actuator Development
Tomohiro Kuroda, AISIN CORPORATION
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DOE-based Optimization of Electrical Machines
Stephan Guenther, IAV GmbH
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Application of Automation of Design Utilizing Optimization to Traction Motor
Tomoya Ueda, NIDEC CORPORATION
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AI Technology with Aid of Data-driven Method Makes EM Simulation and Optimization More Effective
Hajime Igarashi, Hokkaido University
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Reduction of Optimal Design Time for IPMSMs for Automotive Applications Using Machine Learning
Yuki Shimizu, Osaka Prefecture University
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3D Design of Magnetic Components by Gaussian Kernel Regression -Forward Design and Inverse Design-
Yuki Sato, Texas Instruments Japan Limited
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Application of Deep Learning to Optimal Design
Hajime Igarashi, Hokkaido University
