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Topology Optimization Accelerated by Deep Learning

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Title: Topology Optimization Accelerated by Deep Learning
Authors: Sasaki, Hidenori Browse this author
Igarashi, Hajime Browse this author →KAKEN DB
Keywords: Approximate computing
convolutional neural network (CNN)
deep learning (DL)
interior permanent magnet (IPM) motor
topology optimization
Issue Date: Jun-2019
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Transactions on Magnetics
Volume: 55
Issue: 6
Start Page: 7401305
Publisher DOI: 10.1109/TMAG.2019.2901906
Abstract: The computational cost of topology optimization based on the stochastic algorithm is shown to be greatly reduced by deep learning. In the learning phase, the cross-sectional image of an interior permanent magnet motor, represented in RGB, is used to train a convolutional neural network (CNN) to infer the torque properties. In the optimization phase, all the individuals are approximately evaluated by the trained CNN, while finite element analysis for accurate evaluation is performed only for a limited number of individuals. It is numerically shown that the computational cost for the topology optimization can be reduced without the loss of optimization quality.
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article (author version)
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 五十嵐 一

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