Yizhao Huang
Research title: Digital twin for superconducting fault current limiter
The University of Glasgow uses cookies for analytics. Find out more about our Privacy policy.
Necessary cookies enable core functionality. The website cannot function properly without these cookies, and can only be disabled by changing your browser preferences.
Analytical cookies help us improve our website. We use Google Analytics. All data is anonymised.
Clarity helps us to understand our users’ behaviour by visually representing their clicks, taps and scrolling. All data is anonymised.
Research title: Digital twin for superconducting fault current limiter
Huang, Yizhao, Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485 and Song, Wenjuan
ORCID: https://orcid.org/0000-0001-8003-7038
(2025)
A deep learning-based ultra-fast surrogate model for AC loss estimation in superconductors using COMSOL.
IEEE Access,
(doi: 10.1109/ACCESS.2025.3603953)
(Early Online Publication)
Huang, Yizhao, Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485 and Song, Wenjuan
ORCID: https://orcid.org/0000-0001-8003-7038
(2025)
A deep learning-based ultra-fast surrogate model for AC loss estimation in superconductors using COMSOL.
IEEE Access,
(doi: 10.1109/ACCESS.2025.3603953)
(Early Online Publication)