Kejun Tang (唐科军)
 |
PhD, |
About me
Now I am a research scientist at Changsha Institute for Computing and Digital Economy, Peking University. I obtained my PhD degree in the School of Information Science and Technology (SIST), ShanghaiTech University, (ShanghaiTech) under the supervision of Prof.Qifeng Liao. After that, I did postdoctoral research at Peng Cheng Laboratory working with Prof.Chao Yang.
Collaborators
Education
ShanghaiTech University & University of Chinese Academy of Sciences, Sep 2015 - Jan 2021
School of Information Science and Technology
Louisiana State University, Feb 2019 - Aug 2019
Department of Mathematics &
Center for Computation and Technology, visiting student, under the supervision of Professor Xiaoliang Wan
YanTai University, Sep 2011 - July 2015
Department of Mathematics and Information Science
Research
My research interests include tensor methods, machine learning and scientific computing. In particular, I am working on
Tensor computation
Density estimation
Deep learning methods and differential equations
Publications and preprints
X. Wang, K. Tang*, J. Zhai, X. Wan and C. Yang. Deep adaptive sampling for surrogate modeling without labeled data, preprint, 2024
K. Tang, J. Zhai, X. Wan and C. Yang. Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs, The International Conference on Learning Representations (ICLR), 2024
Y. Feng, K. Tang, X. Wan and Q. Liao. Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems, preprint, 2023
P. Yin, G. Xiao, K. Tang and C. Yang. AONN: An adjoint-oriented neural network method for all-at-once solutions of parametric optimal control problems, SIAM Journal on Scientific Computing, accept, 2023
K. Tang, X. Wan and C. Yang. DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations, Journal of Computational Physics, 476 (2023): 111868
X. Wan and K. Tang. Augmented KRnet for density estimation and approximation, arXiv, June, 2021.
K. Tang, X. Wan and Q. Liao. Adaptive deep density approximation for Fokker-Planck equations, Journal of Computational Physics, 457 (2022): 111080.
Y. Feng, K. Tang, L. He, P. Zhou and Q. Liao. Tensor train random projection, Computer Modeling in Engineering and Sciences, 134(2), 1195–1218, 2022.
K. Tang, X. Wan and Q. Liao. Deep density estimation via invertible block-triangular mapping, Theoretical & Applied Mechanics Letters, 10 (3), 143-148, 2020.
K. Tang and Q. Liao. Rank adaptive tensor recovery based model reduction for partial differential equations with high-dimensional random inputs, Journal of Computational Physics, 409 (2020): 109326.
K. Li, K. Tang, T. Wu, and Q. Liao. D3M: A deep domain decomposition method for partial differential equations, IEEE Access, 8 (2019).
K. Li, K. Tang, T. Wu, J. Li and Q. Liao. A hierarchical neural hybrid method for failure probability estimation, IEEE Access, 7 (2019): 112087-112096.