Development of Encoder-Decoder Predicting Search Process of Level-set Method in Magnetic Circuit Design

Ryota Kawamata, Shinji Wakao, Noboru Murata, Yoshifumi Okamoto

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The finite element analysis (FEA) of magnetic field generally requires a lot of calculation time. Especially, design optimization methods such as the level-set method with FEA result in large computational effort to find better solution. In this paper, we propose a novel method of precisely and quickly reproducing the conventional optimization steps by means of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The developed method enables us to implement high speed search of solution, which means the possibility of effective optimization with various initial conditions for better solution. Finally, we evaluate calculation time and computational accuracy of the proposed method by using a magnetic circuit design model.

本文言語English
ホスト出版物のタイトルCOMPUMAG 2019 - 22nd International Conference on the Computation of Electromagnetic Fields
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728155920
DOI
出版ステータスPublished - 2019 7
イベント22nd International Conference on the Computation of Electromagnetic Fields, COMPUMAG 2019 - Paris, France
継続期間: 2019 7 152019 7 19

出版物シリーズ

名前COMPUMAG 2019 - 22nd International Conference on the Computation of Electromagnetic Fields

Conference

Conference22nd International Conference on the Computation of Electromagnetic Fields, COMPUMAG 2019
CountryFrance
CityParis
Period19/7/1519/7/19

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Radiation

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