Data driven governing equations approximation using deep neural networks T Qin, K Wu, D Xiu Journal of Computational Physics 395, 620-635, 2019 | 307 | 2019 |
An exactly divergence-free finite element method for a generalized Boussinesq problem R Oyarzúa, T Qin, D Schötzau IMA journal of numerical analysis 34 (3), 1104-1135, 2014 | 79 | 2014 |
Bound-preserving discontinuous Galerkin methods for relativistic hydrodynamics T Qin, CW Shu, Y Yang Journal of Computational Physics 315, 323-347, 2016 | 62 | 2016 |
Data-driven learning of nonautonomous systems T Qin, Z Chen, JD Jakeman, D Xiu SIAM Journal on Scientific Computing 43 (3), A1607-A1624, 2021 | 53 | 2021 |
Deep learning of parameterized equations with applications to uncertainty quantification T Qin, Z Chen, J Jakeman, D Xiu International Journal of Uncertainty Quantification, 2019 | 47* | 2019 |
Structure-preserving method for reconstructing unknown Hamiltonian systems from trajectory data K Wu, T Qin, D Xiu SIAM Journal on Scientific Computing 42 (6), A3704-A3729, 2020 | 39 | 2020 |
Implicit positivity-preserving high-order discontinuous Galerkin methods for conservation laws T Qin, CW Shu SIAM Journal on Scientific Computing 40 (1), A81-A107, 2018 | 29 | 2018 |
Reducing Parameter Space for Neural Network Training T Qin, L Zhou, D Xiu Theoretical and Applied Mechanics Letters 10 (3), 170-181, 2020 | 5 | 2020 |
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data J Hou, T Qin, K Wu, D Xiu Communications on Applied Mathematics and Computation, 2020 | | 2020 |