I am now working on Physics-Informed Machine Learning and Neural Operators for PDEs, with a focus on efficient surrogate modeling in fluid dynamics, control, and scientific computing. If you are seeking any form of academic cooperation, please feel free to email me at 0923B09@zju.edu.cn.

I graduated from Xiamen University (厦门大学) with a bachelor’s degree in Aerospace Engineering, and from Zhejiang University (浙江大学) with a master’s degree in Control Science and Engineering.

During my studies, I worked closely with Chao Xu (许超), Shengze Cai (蔡声泽) and collaborators on integrating control theory and deep learning, with applications in scientific machine learning and optimization.

I was recognized with several honors, including:

• 2024 Fifth place (5/10) in the IJCAI Aerodynamic Speed Prediction Competition (CCF A)
• 2021 Outstanding Graduate Student Leader, Zhejiang University
• 2018 Second Prize in the NXP Cup National College Students’ Intelligent Car Competition (South China Division)
• 2017 China National Encouragement Scholarship, Xiamen University

My research interests include neural differential equations, graph transformers for scientific learning, and physics-informed surrogate models. I have published papers in some journals and conferences such as Nature Communications, Neurocomputing, AAAI, IEEE Transactions on Network Science and Engineering.

To promote academic communication, I actively engage in interdisciplinary collaborations at the intersection of artificial intelligence, control theory, and fluid dynamics, aiming to develop efficient and generalizable PDE foundation models.