معرفی
Guang Hu is an Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), affiliated with the Energy Technology group, EIRES, and EAISI. His research integrates computational modeling, machine learning, and sustainable energy systems to address urban environmental challenges.
- Ph.D. in Nuclear and New Energy Technology, Tsinghua University (2019)
- Postdoctoral Researcher, Karlsruhe Institute of Technology, Germany
- Researcher, Paul Scherrer Institut (PSI), Switzerland
Guang Hu's research focuses on machine learning-assisted modeling, multi-scale modeling, and sustainable energy applications, particularly in thermal and nuclear energy systems. His work aims to enhance urban resilience and energy efficiency through data-driven and physics-informed computational methods. He is actively involved in advancing solutions for nuclear waste management, solar energy integration in aging urban environments, and thermal-hydraulic process optimization.
The recent publications highlight a strong trend in applying machine learning and surrogate modeling to complex energy systems, especially in nuclear waste management and urban solar energy. His work bridges computational efficiency with real-world sustainability challenges, emphasizing practical applications in full-scale experiments and geochemical processes. The interdisciplinary nature of his research spans nuclear engineering, urban sustainability, and artificial intelligence.
Scientific Awards and Recognition:
- Ph.D. with distinction, Tsinghua University
- Active reviewer for leading journals in computational modeling and energy
- Contributor to UN Sustainable Development Goals (SDGs) in clean energy and sustainable cities
Guang Hu actively mentors students and collaborates internationally, though specific advisees are not listed. He has participated in significant research collaborations with institutions in Germany and Switzerland and contributes to major conferences such as ICONE and EU PVSEC. His work is supported by ongoing research activities within TU/e’s energy and AI institutes. While specific grants are not detailed, his funding is inferred through sustained research output and international partnerships.
He is associated with research initiatives including digital twin development for nuclear waste, surrogate modeling for geochemical processes, and performance assessment of solar systems in Dutch urban contexts. These efforts are part of broader teams at TU/e focused on energy transition, AI for engineering, and sustainable infrastructure.
