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Qingguo Hong
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Cited by
Year
PARAMETER-ROBUST STABILITY OF CLASSICAL THREE-FIELD FORMULATION OF BIOT’S CONSOLIDATION MODEL
Q Hong, J Kraus
Electronic Transactions on Numerical Analysis 48, 202-226, 2018
902018
Conservative discretizations and parameter-robust preconditioners for Biot and multiple-network flux-based poroelasticity models
Q Hong, J Kraus, M Lymbery, F Philo
Numerical Linear Algebra with Applications, 2019
532019
A robust multigrid method for discontinuous Galerkin discretizations of Stokes and linear elasticity equations
Q Hong, J Kraus, J Xu, L Zikatanov
Numerische Mathematik 132 (1), 23-49, 2016
522016
A unified study of continuous and discontinuous Galerkin methods.
Q Hong, F Wang, S Wu, J Xu
Science China: Mathematics. 62, 1-32, 2019
432019
A discontinuous Galerkin method for the fourth-order curl problem
Q Hong, J Hu, S Shu, J Xu
Journal of Computational Mathematics, 565-578, 2012
392012
Parameter-robust convergence analysis of fixed-stress split iterative method for multiple-permeability poroelasticity systems
Q Hong, J Kraus, M Lymbery, MF Wheeler
Multiscale Modeling & Simulation 18 (2), 916-941, 2020
322020
Greedy training algorithms for neural networks and applications to PDEs
JW Siegel, Q Hong, X Jin, W Hao, J Xu
arXiv preprint arXiv:2107.04466, 2021
312021
Uniformly stable discontinuous Galerkin discretization and robust iterative solution methods for the Brinkman problem
Q Hong, J Kraus
SIAM Journal on Numerical Analysis 54 (5), 2750-2774, 2016
312016
Two-grid economical algorithms for parabolic integro-differential equations with nonlinear memory
W Wang, Q Hong
Applied Numerical Mathematics 142, 28-46, 2019
242019
A priori analysis of stable neural network solutions to numerical PDEs
Q Hong, JW Siegel, J Xu
arXiv preprint arXiv:2104.02903, 2021
212021
On the activation function dependence of the spectral bias of neural networks
Q Hong, JW Siegel, Q Tan, J Xu
arXiv preprint arXiv:2208.04924, 2022
192022
Parameter-robust Uzawa-type iterative methods for double saddle point problems arising in Biot's consolidation and multiple-network poroelasticity models
Q Hong, J Kraus, M Lymbery, F Philo
Mathematical Models and Methods in Applied Sciences 30 (13), 2523-2555, 2020
182020
Greedy training algorithms for neural networks and applications to PDEs
JW Siegel, Q Hong, X Jin, W Hao, J Xu
Journal of Computational Physics 484, 2023
152023
An extended Galerkin analysis in finite element exterior calculus
Q Hong, Y Li, J Xu
Mathematics of Computation 91 (335), 1077-1106, 2022
112022
Robust block preconditioners for poroelasticity
S Chen, Q Hong, J Xu, K Yang
Computer Methods in Applied Mechanics and Engineering 369, 113229, 2020
102020
A new practical framework for the stability analysis of perturbed saddle-point problems and applications
Q Hong, J Kraus, M Lymbery, F Philo
Mathematics of Computation 92 (340), 607-634, 2023
92023
An extended Galerkin analysis for elliptic problems
Q Hong, S Wu, J Xu
Science China Mathematics, 1-18, 2021
92021
Uniform stability and error analysis for some discontinuous Galerkin methods
Q Hong, J Xu
Journal of Computational Mathematics 39 (2), 283-310, 2021
92021
A new framework for the stability analysis of perturbed saddle-point problems and applications in poromechanics
Q Hong, J Kraus, M Lymbery, F Philo
arXiv preprint arXiv:2103.09357, 2021
62021
An extended Galerkin analysis for linear elasticity with strongly symmetric stress tensor
Q Hong, J Hu, L Ma, J Xu
arXiv preprint arXiv:2002.11664, 2020
62020
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