Open AccessCritical current and n-value prediction of second-generation high temperature superconducting conductors considering the temperature-field dependence based on the back propagation neural network with encoder
Author(s) -
Lingfeng Zhu,
Yinshun Wang,
Ziqing Meng,
Tianjing Wang
Publication year - 2022
Publication title -
superconductor science and technology/superconductor science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.033
H-Index - 105
eISSN - 1361-6668
pISSN - 0953-2048
DOI - 10.1088/1361-6668/ac88fc
Subject(s) - electrical conductor , artificial neural network , encoder , current (fluid) , superconductivity , interpolation (computer graphics) , field (mathematics) , computer science , materials science , physics , condensed matter physics , mathematics , artificial intelligence , thermodynamics , motion (physics) , pure mathematics , composite material , operating system