About me

Tao Zhang (张涛 in Chinese) is currently a master’s student at the Nuclear Power Institute of China (NPIC), under the guidance of Professor Jiao Yongjun. During his master’s program, he visited the AI for Scientific Simulation and Discovery Lab at Westlake University, where he was mentored by Professor Tailin Wu. In 2022, he completed his undergraduate studies at Tsinghua University, majoring in nuclear engineering and technology and minoring in computer science.

His research focuses on: AI for science & nuclear engineering.

Publications

Journal

• Tao Zhang, Wenbing Han, Pengfei Shen, et.al. Neutronic and Thermal-Hydraulic Performance Analysis of Helical Cruciform Fuel Rods[J]. Nuclear power engineering, 2023.
• Tao Zhang, Yongjun Jiao, Zhenhai Liu, et.al. Modeling of zirconium alloy cladding corrosion behavior based on neural ordinary differential equation[J]. Nuclear Engineering and Technology, 2025, 57(3): 103251.
• Tao Zhang, Yongjun Jiao, Zhenhai Liu, et.al. Learning to simulate quasi-steady-state fuel rod performance with hybrid Fourier neural operator[J]. Progress in Nuclear energy, 2025, 186: 105769.
• Tao Zhang, Yongjun Jiao, Zhenhai Liu, et.al. The Current Status and Development Trends of Artificial Intelligence in the Field of Nuclear Fuel and Materials[J]. Rare metal materials and engineering. 2025.

Conference

• Tao Zhang, Yongjun Jiao, Zhenhai Liu, et.al. A Surrogate Model of Steady-state Fuel Performance based on deep learning[C]. Topfuel2024, Franch, 2024.
• Tao Zhang, Yongjun Jiao, Zhenhai Liu, et.al. Uncertainty quantification of corrosion model for zirconium alloy cladding based on artificial intelligence[C]. International Conference of Nuclear Engineering, 2025.
• Tao Zhang, Zhenhai Liu, Feipeng Qi, et.al. M2PDE: Compositional generative multiphysics and multi-compomnent PDE sinmulation[C]. International Conference of Machine Learning, 2025. (IF: 32.4)

Preprint

• Tao Zhang, Zhenhai Liu, Yong Xin, et al. MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation[J]. arXiv preprint arXiv:2504.08621, 2025.

Education

• 2018-2022: Tsinghua University (清华大学), Bachelor of Nuclear Engineering and Nuclear Technology(main) and Computer Science(minor).
• 2022-Now, Nuclear power institude of China, Master of Nuclear Energy Science and engineering.

Reward

• Tsinghua University University-level Award for Outstanding Graduation Design. (top 5%)
• Tsinghua University Award for Academic Excellence.
• Outstanding Report Award at the 4th Nuclear Energy PhD Forum of the Chinese Nuclear Society.

Open-source projects:

• M2PDE: https://github.com/AI4Science-WestlakeU/M2PDE
• MooseAgent: https://github.com/taozhan18/MooseAgent

Skills

• Core Domain Areas:
  • Nuclear fuel and materials, Reactor Physics, Thermal Hydraulics.
• Engineering Simulation & Modeling:
  • Proficiency in: MOOSE, ANSYS, RMC.
  • Surrogate Modeling and Uncertainty Quantification (UQ).
• Applied AI & Machine Learning:
  • Classical Machine Learning Algorithms.
  • Deep Learning Techniques: Neural Operators, Graph Neural Networks (GNNs), Generative Models (Diffusion model).
  • Large Language Models (LLMs): Multi-agent Workflow Design (langGraph).