Aiqing Zhu (祝爱卿)

I am a postdoc in the Department of Mathematics at National University of Singapore.

Email: zaq@nus.edu.sg, zaq@lsec.cc.ac.cn.

Experience

• July. 2023 - present: Department of Mathematics, National University of Singapore, Singapore.
  • Research Fellow (postdoc), mentored by Prof. Qianxiao Li.

Education

• Aug. 2018 - July. 2023: Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
  • Ph.D. in Computational Mathematics, advised by Prof. Yifa Tang.
• Aug. 2014 – July. 2018: University of Science and Technology of China, Hefei, Anhui, China.
  • B.S. in Mathematics.

Research interest:

Symplectic geometric algorithms for Hamiltonian systems, Machine learning and dynamical systems, Machine learning for science.

Publications

[17] Aiqing Zhu, Yuting Pan, Qianxiao Li, Continuity-Preserving Autoencoders for Learning Continuous Latent Dynamical Models from Images. The Thirteenth International Conference on Learning Representations (ICLR2025), PDF, CODE.

[16] Jianfei Huang, Na Liu, Yifa Tang, Ruili Zhang, Aiqing Zhu, Beibei Zhu, Forty years: Geometric numerical integration of dynamical systems in China. International Journal of Modeling, Simulation, and Scientific Computing, 2550009 (2024), PDF.

[15] Sidi Wu, Aiqing Zhu, Yifa Tang, Benzhuo Lu, Solving parametric elliptic interface problems via interfaced operator network. Journal of Computational Physics, 514 (2024) 113217, PDF, arXiv, CODE.

[14] Aiqing Zhu, Tom Bertalan, Beibei Zhu, Yifa Tang, Ioannis G. Kevrekidis, Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets. SIAM Journal on Applied Dynamical Systems, 23(4): 2643-2669 (2024), PDF, arXiv.

[13] Aiqing Zhu, Qianxiao Li, DynGMA: a robust approach for learning stochastic differential equations from data. Journal of Computational Physics, 513 (2024) 113200, PDF, arXiv, CODE.

[12] Aiqing Zhu, Sidi Wu, and Yifa Tang, Error analysis based on inverse modified differential equations for discovery of dynamics using linear multistep methods and deep learning. SIAM Journal on Numerical Analysis, 62 (5): 2087–2120 (2024). PDF, arXiv, CODE.

[11] Jiawei Zhang, Aiqing Zhu, Feng Ji, Chang Lin, Yifa Tang, Effective Numerical Simulations of Synchronous Generator System. Simulation, 100(6): 595-611 (2024). PDF, arXiv.

[10] Sidi Wu, Aiqing Zhu, Yifa Tang, Benzhuo Lu, Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems. Communications in Computational Physics, 33(2): 596-627 (2023). PDF.

[9] Beibei Zhu, Lun Ji, Aiqing Zhu, Yifa Tang, Explicit K-symplectic methods for nonseparable non-canonical Hamiltonian systems. Chinese Physics B, 32 (2): 20204-020204 (2023). PDF.

[8] Beibei Zhu, Lun Ji, Aiqing Zhu, Yifa Tang, Poisson Integrators Based on Splitting Method for Poisson Systems. Communications in Computational Physics, 32(4):1129-1155 (2022). PDF.

[7] Beibei Zhu, Jian Liu, Jiawei Zhang, Aiqing Zhu, Yifa Tang, Adaptive energy-preserving algorithms for guiding center system. Plasma Science and Technology, 25, 045102 (2022). PDF.

[6] Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang, On Numerical Integration in Neural Ordinary Differential Equations. Proceedings of the 39th International Conference on Machine Learning (ICML2022), PMLR 162, 27527-27547 (2022). PDF, CODE.

[5] Aiqing Zhu, Beibei Zhu, Jiawei Zhang, Yifa Tang, Jian Liu, VPNets: Volume-preserving neural networks for learning source-free dynamics. Journal of Computational and Applied Mathematics, 416, 114523 (2022). PDF, arXiv, CODE.

[4] Aiqing Zhu, Pengzhan Jin, Yifa Tang, Approximation capabilities of measure-preserving neural networks. Neural Networks, 147(2022) 72-80. PDF, arXiv, CODE.

[3] Pengzhan Jin, Yifa Tang, Aiqing Zhu, Unit triangular factorization of the matrix symplectic group. SIAM Journal on Matrix Analysis and Applications, 41(4), 1630-1650 (2020). PDF, arXiv.

[2] Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis, SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. Neural Networks, 132, 166-179 (2020). PDF, arXiv, CODE.

[1] Aiqing Zhu, Pengzhan Jin, Yifa Tang, Deep Hamiltonian networks based on symplectic integrators (in Chinese). Mathematica Numerica Sinica, 42(3), 370-384 (2020), special issue in memery of Prof. Kang Feng on his centennial birthday. PDF, English version.

Teaching

• Spring 2020, Fall 2020: TA, Mathmatical Analysis, University of Chinese Academy of Sciences.

• Spring 2017, Fall 2017: TA, Mathmatical Analysis, University of Science and Technology of China.

Professional Service

Conference reviewer: NeurIPS, ICML.