Application of Conditional Variational Auto-Encoder to Magnetic Circuit Design with Magnetic Field Computation

Ryota Kawamata, Shinji Wakao, Noboru Murata

研究成果: Conference contribution

抄録

In the design optimization of electric machine, we ordinarily derive the objective physical quantities, e.g., the shape of the investigated model as design variables, by using numerical method such as the finite element method with the analysis conditions. In recent years, the representation learning using Deep Learning much attracts attention because it can acquire the features of data as a distributed representation and reproduce corresponding data. In this paper, utilizing machine learning technology, we propose an application of Conditional Variational Auto-Encoder (CVAE) to reproduce the more adequate shape of magnetic materials, i.e., design variables, corresponding to the intended magnetic energy, i.e., objective function values.

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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