Guang Hu
Source: https://www.tue.nl/en/research/researchers/guang-hu Parent: https://www.tue.nl/en/news-and-events/news-overview/12-11-2025-super-powered-ai-from-eindhoven-helps-doctors-identify-cancer-and-other-diseases-more-quickly
Assistant Professor
Guang Hu
Contact
Vector 3.212
Department / Institute
Group
Group Reinders
EIRES Research
RESEARCH PROFILE
Guang Hu is an Assistant Professor of Energy Technology Group, in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e). His main areas of expertise are machine learning-assisted modeling, multi-scale modeling, sustainable energy applications, and thermal/nuclear energy systems. He is focused on developing practical solutions that address environmental challenges in urban settings through computational methods and data-driven approaches.\ The intersection of computational modeling, machine learning, and sustainable energy represents a frontier in addressing climate challenges. Through his research, Guang aims to develop solutions that can make urban environments more resilient and energy-efficient.
At the intersection of machine learning and energy systems, we find innovative solutions to our most pressing environmental challenges.
ACADEMIC BACKGROUND
Guang Hu obtained his Ph.D. with distinction from Tsinghua University in 2019. Prior to joining TU/e, he held positions as a postdoctoral researcher at the Karlsruhe Institute of Technology in Germany and as a researcher at the Paul Scherrer Institut (PSI) in Switzerland, building a strong international research profile.
Guang has published in several respected journals in the fields of computational modeling, machine learning applications, and sustainable/nuclear energy. His research contributions span theoretical advancements in modeling techniques and practical applications for energy systems. He actively participates in international research collaborations and serves as a reviewer for journals in his field, contributing to the academic community's knowledge development and quality assurance.
Recent Publications
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[### Physics-based machine learning for subcooled boiling flow prediction with DEBORA experiment
AI Thermal Fluids
(2026)
Guang Hu](https://research.tue.nl/nl/publications/3e2c5aad-9200-4b0d-9443-ea7820a6b69c) - [### Machine Learning-Based Prediction of Photovoltaic Power Generation A Case Study Using Two-year Time Series Amsterdam Weather Data and SAM Simulations
42nd European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC)
(2025)
Guang Hu,Roel C.G.M. Loonen,Angèle H.M.E. Reinders](https://research.tue.nl/nl/publications/3696e9d5-7105-4464-8252-fa8f6eea762a) - [### Analyzing the Influence of Weather Conditions and Solar Irradiance on Photovoltaic Power Generation: A Case Study in Amsterdam
12th International Conference on Urban Climate
(2025)
Guang Hu,Roel C.G.M. Loonen,Angèle H.M.E. Reinders](https://research.tue.nl/nl/publications/71e8b6ea-c0ad-4a27-ae3a-7b6c0ea649df) - [### HEat Robustness In relation To AGEing cities (HERITAGE) Program
12th International Conference on Urban Climate
(2025)
W. Timmermans,S. Gadde,Heet Joshi,Sander Oude Elberink,M. Büyükdemircioğlu,G.J. Steeneveld,D. Milošević,B. Sandvik,R. Uijlenhoet,A. Droste](https://research.tue.nl/nl/publications/4f128232-e219-43da-ba70-f2cf384a9c1c) - [### Physics-Based and Data-Driven Digital Twins for 3D-Temperature Evolution in the Near-field of the FE Tunnel at Mont Terri
(2024)
Wilfried Pfingsten,Guang Hu](https://research.tue.nl/nl/publications/93faa704-595d-487b-bf73-4b631edce184)
Ancillary Activities
No ancillary activities