Xiaoge Tian
Ph.D. Candidate, HKU SAIL
Email: xiaogetian (at) connect.hku.hk
Office: HKU Composite building
Biography
Xiaoge Tian
(田晓歌)
is a second-year Ph.D. student at HKU SAIL
supervised by Prof. Jiaji Wang.
Previously, he obtained his master's degree under Prof. Chen's supervision at China University of Geosciences(Wuhan).
Before his master's degree, he majored in Logistics Management at Huazhong University of Science and Technology supervised by Prof. Wang.
His research goal covers the whole pipeline of physics-informed machine learning:
(1) Physics-informed neural network,
(2) Physics-informed reinforcement learning,
and (3) Deep reinforecement learning with operation research.
[...]
Research
(* indicates corresponding author, highlight indicates representative papers)
@article{TIAN2025119884,
title={Automated machine learning exact dirichlet boundary physics-informed neural networks for solid mechanics},
author={Tian, Xiaoge., Wang, Jiaji., Kim, Chul-Woo., Deng, Xiaowei., and Zhu, Yingjie},
journal={Engineering Structures},
volume={330},
pages={119884},
year={2025},
publisher={Elsevier}
}
@article{tian2023game,
title={Game-theoretic modeling of power supply chain coordination under demand variation in China: A case study of Guangdong Province},
author={Tian, Xiaoge and Chen, Weiming and Hu, Jinglu},
journal={Energy},
volume={262},
pages={125440},
year={2023},
publisher={Elsevier}
}
@article{xie2021vulnerability,
title={The vulnerability of the power grid structure: A system analysis based on complex network theory},
author={Xie, Banghua and Tian, Xiaoge and Kong, Liulin and Chen, Weiming*},
journal={Sensors},
volume={21},
number={21},
pages={7097},
year={2021},
publisher={MDPI}
}
Education
B.BA. in Logistics Management
Sep. 2015 - Jun. 2019
Wuhan, China
M.Eng. in Safety Science and Engineering
Sep. 2020 - Jun. 2023
Wuhan, China
Experience