Yahao Wu
Email: 2771585W@student.gla.ac.uk
Address: 528, James Watt South Building, University of Glasgow, Glasgow, United Kingdom, G12 8QQ
https://orcid.org/0009-0006-4264-317X
Research title: Intelligent quench detection of superconducting systems
Research summary
I am a PhD researcher at the University of Glasgow working on intelligent quench detection in high-temperature superconducting (HTS) systems.
Publications
2026
Wu, Yahao, Song, Wenjuan 
ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2026)
Machine learning prediction for experimental quench voltage of HTS coils using a combined framework with time-series generative adversarial network and long short-term memory techniques.
IEEE Transactions on Applied Superconductivity, 36(5),
4701105.
(doi: 10.1109/TASC.2025.3646171)
Wu, Yahao, Fang, Lurui, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038, Wu, Yue, Jiang, Zhenan and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2026)
First artificial intelligence-based non-invasive framework for data-driven kink defect detection in HTS coils for power applications.
Superconductivity,
(doi: 10.1016/j.supcon.2026.100242)
(In Press)
2025
Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Physics-guided quench detection framework for HTS coils based on augmentation of artificial quench data: A comprehensive benchmarking investigation on 75 different machine learning and dimensionality reduction techniques.
Superconductor Science and Technology,
(doi: 10.1088/1361-6668/ae26d7)
(In Press)
Alipour Bonab, Shahin ORCID: https://orcid.org/0009-0002-8316-4336, Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiations for fusion energy applications: first machine learning study.
Superconductor Science and Technology, 38,
09LT01.
(doi: 10.1088/1361-6668/ae011e)
Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Advanced intelligent quench diagnostics for high temperature superconducting coils based on principal component analysis of voltage harmonic ratios and Support Vector Machine.
Superconductivity, 14,
100173.
(doi: 10.1016/j.supcon.2025.100173)
Articles
Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2026)
Machine learning prediction for experimental quench voltage of HTS coils using a combined framework with time-series generative adversarial network and long short-term memory techniques.
IEEE Transactions on Applied Superconductivity, 36(5),
4701105.
(doi: 10.1109/TASC.2025.3646171)
Wu, Yahao, Fang, Lurui, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038, Wu, Yue, Jiang, Zhenan and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2026)
First artificial intelligence-based non-invasive framework for data-driven kink defect detection in HTS coils for power applications.
Superconductivity,
(doi: 10.1016/j.supcon.2026.100242)
(In Press)
Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Physics-guided quench detection framework for HTS coils based on augmentation of artificial quench data: A comprehensive benchmarking investigation on 75 different machine learning and dimensionality reduction techniques.
Superconductor Science and Technology,
(doi: 10.1088/1361-6668/ae26d7)
(In Press)
Alipour Bonab, Shahin ORCID: https://orcid.org/0009-0002-8316-4336, Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiations for fusion energy applications: first machine learning study.
Superconductor Science and Technology, 38,
09LT01.
(doi: 10.1088/1361-6668/ae011e)
Wu, Yahao, Song, Wenjuan ORCID: https://orcid.org/0000-0001-8003-7038 and Yazdani-Asrami, Mohammad ORCID: https://orcid.org/0000-0002-7691-3485
(2025)
Advanced intelligent quench diagnostics for high temperature superconducting coils based on principal component analysis of voltage harmonic ratios and Support Vector Machine.
Superconductivity, 14,
100173.
(doi: 10.1016/j.supcon.2025.100173)
Grants
Bill Nicol Engineering Scholarship
University of Glasgow
2025-2026
Teaching
Course
- Simulation of Engineering Systems 3 (Demonstrator)
