TY - GEN
T1 - Application of Conditional Variational Auto-Encoder to Magnetic Circuit Design with Magnetic Field Computation
AU - Kawamata, Ryota
AU - Wakao, Shinji
AU - Murata, Noboru
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Design optimization
KW - conditional variational auto-encoder
KW - magnetic circuit
KW - representation learning
UR - http://www.scopus.com/inward/record.url?scp=85083155848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083155848&partnerID=8YFLogxK
U2 - 10.1109/COMPUMAG45669.2019.9032766
DO - 10.1109/COMPUMAG45669.2019.9032766
M3 - Conference contribution
AN - SCOPUS:85083155848
T3 - COMPUMAG 2019 - 22nd International Conference on the Computation of Electromagnetic Fields
BT - COMPUMAG 2019 - 22nd International Conference on the Computation of Electromagnetic Fields
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on the Computation of Electromagnetic Fields, COMPUMAG 2019
Y2 - 15 July 2019 through 19 July 2019
ER -