Scholar
Kejun Tang (唐科军)
Google Scholar ID: t5Bxl1kAAAAJ
Great Bay University
Citations & Impact
Citations
515
H-index
8
i10-index
7
Publications
16
Co-authors
0
Contact
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Estimating committor functions via deep adaptive sampling on rare transition paths, Journal of Computational Physics, 2026
- Functional tensor train neural network for solving high-dimensional PDEs, Preprint, 2025
- Provable low-rank tensor-train approximations in the inverse of large-scale structured matrices, accepted by Mathematics of Computation, 2025
- APTT: An accuracy-preserved tensor-train method for the Boltzmann-BGK equation, SIAM Journal on Scientific Computing, 2025
- Deep adaptive sampling for surrogate modeling without labeled data, Journal of Scientific Computing, 2024
- Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs, The International Conference on Learning Representations (ICLR), 2024
- AONN: An adjoint-oriented neural network method for all-at-once solutions of parametric optimal control problems, SIAM Journal on Scientific Computing, 2024
- Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems, preprint, 2023
- DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations, Journal of Computational Physics, 2023
- Augmented KRnet for density estimation and approximation, arXiv, 2021
- Adaptive deep density approximation for Fokker-Planck equations, Journal of Computational Physics, 2022
- Tensor train random projection, Computer Modeling in Engineering and Sciences, 2022
- Deep density estimation via invertible block-triangular mapping, Theoretical & Applied Mechanics Letters, 2020
- Rank adaptive tensor recovery based model reduction for partial differential equations with high-dimensional random inputs, Journal of Computational Physics
Background
- Currently a faculty member at Great Bay University (GBU). Research interests include tensor methods, machine learning, and scientific computing, particularly low-rank tensor methods and applications, density estimation and deep generative models, deep learning methods and differential equations.
Miscellany
- Currently looking for PhD students, postdoctoral fellows, and visiting students to work with. If interested, please feel free to send an email with your CV.
Co-authors