A Review of Data-driven Surrogate Models for Design Optimization of Electric Motors

Cheng, Mengyu, Zhao, Xing orcid.org/0000-0003-4000-0446, Dhimish, Mahmoud et al. (2 more authors) (2024) A Review of Data-driven Surrogate Models for Design Optimization of Electric Motors. IEEE Transactions on Transportation Electrification. ISSN: 2332-7782

Metadata

Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.
Dates:
  • Accepted: 1 February 2024
  • Published (online): 15 February 2024
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Electronic Engineering (York)
Date Deposited: 19 Feb 2024 17:20
Last Modified: 29 Oct 2025 00:16
Published Version: https://doi.org/10.1109/TTE.2024.3366417
Status: Published online
Refereed: Yes
Identification Number: 10.1109/TTE.2024.3366417
Open Archives Initiative ID (OAI ID):

Download

Accepted Version

Filename: A_Review_of_Data-driven_Surrogate_Models_for_Design_Optimization_of_Electric_Motors.pdf

Description: A_Review_of_Data-driven_Surrogate_Models_for_Design_Optimization_of_Electric_Motors

Licence: CC-BY 2.5

CLICK TO DOWNLOAD [thumbnail of A_Review_of_Data-driven_Surrogate_Models_for_Design_Optimization_of_Electric_Motors]

Export

Statistics


CORE (COnnecting REpositories)