Tong Qin

Tong Qin

PhD in Applied Math

Publications

Google Scholar

Refereed Journal Publications

  1. T. Qin, Z. Chen, J. Jakeman, and D. Xiu. Data-driven learning of non-autonomous systems, SIAM Journal on Scientific Computing, 43(3), A1607-A1624, 2021. [arXiv] [journal]
  2. T. Qin, Z. Chen, J. Jakeman, and D. Xiu. Deep learning of parameterized equations with applications to uncertainty quantification, International Journal for Uncertainty Quantification, 11(2), 63-82, 2021. [arXiv] [journal]
  3. K. Wu, T. Qin, and D. Xiu. Structure-preserving method for reconstructing unknown Hamiltonian systems from data, SIAM Journal on Scientific Computing, 42(6), A3704-A3729, 2020. [arXiv] [journal]
  4. J. Hou, T. Qin, K. Wu, and D. Xiu. A non-intrusive correction algorithm for classification problems with corrupted data, Communications on Applied Mathematics and Computation, 2020. [arXiv] [journal]
  5. T. Qin, L. Zhou, and D. Xiu. Reducing parameter space for neural network training, Theoretical and Applied Mechanics Letters 10(3), 170-181, 2020. [arXiv] [journal]
  6. T. Qin, K. Wu, and D. Xiu. Data driven governing equations approximation using deep neural networks, Journal of Computational Physics 395, 620-635, 2019. [arXiv] [journal]
  7. T. Qin and C.-W. Shu, Implicit positivity-preserving high-order discontinuous Galerkin methods for conservation laws, SIAM Journal on Scientific Computing 40(1), A81-A107, 2018. [journal]
  8. T. Qin, Y. Yang, and C.-W. Shu. Bound-preserving discontinuous Galerkin methods for relativistic hydrodynamics, Journal of Computational Physics 315, 323-347, 2016. [journal]
  9. R. Oyarzúa, T. Qin, and D. Schötzau. An exactly divergence-free finite element method for a generalized Boussinesq problem, IMA Journal of Numerical Analysis 34 (3), 1104-1135, 2014. [journal]