Research Interests

My research interests include scientific machine learning and uncertainty quantification, with a particular focus on deep generative model-based methods for the numerical solution of the Fokker-Planck equation.

Education

• Sep. 2018 -- Jun. 2023, Ph.D. in Computational Mathematics, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China. (Supervisor: Prof. Tao Zhou)
• Sep. 2014 -- Jun. 2018, B.S. in Mathematics and Applied Mathematics, School of Mathematical Sciences, Beijing Normal University, Beijing, China. (Supervisors: Prof. Zhengru Zhang and Prof. Tao Zhou)

Employment

• Aug. 2023 -- Now, Associate professor, School of Mathematics and Statistics, Fuzhou University, Fuzhou, Fujian, China.
• Dec. 2023 -- Nov. 2025, Postdoctoral researcher, CSQI, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. (Host: Prof. Fabio Nobile)

Grants

• Jun. 2024 -- May 2026, Deep adaptive algorithm for Fokker-Planck equations, Fuzhou University, No.511453 (PI, RMB 300,000).
• Jan. 2025 -- Dec. 2027, Deep adaptive algorithm and uncertainty quantification for the time-dependent Fokker-Planck equations, NSFC, No.12401566 (PI, RMB 300,000).

Students

• Master students: Jiangfan Chen (2024-2027), Jinguo Zhao (2025-2028)

Publications

• Li Zeng, Xiaoliang Wan, Tao Zhou. Bounded KRnet and its applications to density estimation and approximation, SIAM Journal on Scientific Computing, 47(6), C1294-1318, 2025.
• Li Zeng, Xiaoliang Wan, Tao Zhou. Adaptive deep density approximation for fractional fokker–planck equations, Journal of Scientific Computing, 97(3), 68, 2023.
• Xiaodong Feng, Li Zeng. Gradient-enhanced deep neural network approximations, Journal of Machine Learning for Modeling and Computing, 3(4), 2022.
• Xiaodong Feng, Li Zeng, Tao Zhou. Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow, Communications in Computational Physics, 32(2), 401-423, 2022.