Qingguo Hong

614 total citations
21 papers, 351 citations indexed

About

Qingguo Hong is a scholar working on Computational Mechanics, Mechanics of Materials and Computational Theory and Mathematics. According to data from OpenAlex, Qingguo Hong has authored 21 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computational Mechanics, 14 papers in Mechanics of Materials and 6 papers in Computational Theory and Mathematics. Recurrent topics in Qingguo Hong's work include Advanced Numerical Methods in Computational Mathematics (19 papers), Numerical methods in engineering (14 papers) and Electromagnetic Simulation and Numerical Methods (5 papers). Qingguo Hong is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (19 papers), Numerical methods in engineering (14 papers) and Electromagnetic Simulation and Numerical Methods (5 papers). Qingguo Hong collaborates with scholars based in United States, China and Germany. Qingguo Hong's co-authors include Johannes Kraus, Jinchao Xu, Shuonan Wu, Fei Wang, Jinchao Xu, Wansheng Wang, Ludmil Zikatanov, Jonathan W. Siegel, Wenrui Hao and Mary F. Wheeler and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Mathematics of Computation.

In The Last Decade

Qingguo Hong

20 papers receiving 333 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qingguo Hong United States 10 274 180 121 62 38 21 351
Michael Karkulik Chile 11 251 0.9× 212 1.2× 98 0.8× 125 2.0× 12 0.3× 26 408
Robert Altmann Germany 11 122 0.4× 79 0.4× 86 0.7× 48 0.8× 32 0.8× 36 339
Ignacio Muga Chile 12 254 0.9× 160 0.9× 115 1.0× 116 1.9× 17 0.4× 36 407
Hyea Hyun Kim South Korea 14 430 1.6× 236 1.3× 217 1.8× 172 2.8× 18 0.5× 42 480
Marc Duruflé France 12 142 0.5× 102 0.6× 48 0.4× 133 2.1× 56 1.5× 27 367
Markus Aurada Austria 8 174 0.6× 124 0.7× 60 0.5× 82 1.3× 6 0.2× 14 274
Jean‐Pierre Croisille France 14 407 1.5× 80 0.4× 100 0.8× 55 0.9× 22 0.6× 37 511
Samuel Ferraz-Leite Austria 7 180 0.7× 133 0.7× 60 0.5× 92 1.5× 5 0.1× 10 287
F. Nataf France 11 331 1.2× 120 0.7× 220 1.8× 142 2.3× 29 0.8× 20 428
Abdeljalil Nachaoui France 11 122 0.4× 205 1.1× 164 1.4× 27 0.4× 25 0.7× 56 442

Countries citing papers authored by Qingguo Hong

Since Specialization
Citations

This map shows the geographic impact of Qingguo Hong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qingguo Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingguo Hong more than expected).

Fields of papers citing papers by Qingguo Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qingguo Hong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qingguo Hong. The network helps show where Qingguo Hong may publish in the future.

Co-authorship network of co-authors of Qingguo Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Qingguo Hong. A scholar is included among the top collaborators of Qingguo Hong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qingguo Hong. Qingguo Hong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hong, Qingguo, et al.. (2025). Greedy Algorithm for Neural Networks for Indefinite Elliptic Problems. Journal of Scientific Computing. 104(3). 1 indexed citations
2.
Hong, Qingguo, et al.. (2024). Phase-field model of strain effect on superconducting transitions and mesoscale pattern formation. Computational Materials Science. 236. 112814–112814. 1 indexed citations
3.
Hao, Wenrui, et al.. (2024). Gauss Newton Method for Solving Variational Problems of PDEs with Neural Network Discretizaitons. Journal of Scientific Computing. 100(1). 2 indexed citations
4.
Siegel, Jonathan W., et al.. (2023). Greedy training algorithms for neural networks and applications to PDEs. Journal of Computational Physics. 484. 112084–112084. 28 indexed citations
5.
Hong, Qingguo, et al.. (2022). Robust Approximation of Generalized Biot-Brinkman Problems. Journal of Scientific Computing. 93(3). 5 indexed citations
6.
Hong, Qingguo, et al.. (2022). An efficient iterative method for dynamical Ginzburg-Landau equations. Journal of Computational Physics. 474. 111794–111794. 4 indexed citations
7.
Hong, Qingguo, Yuwen Li, & Jinchao Xu. (2021). An extended Galerkin analysis in finite element exterior calculus. Mathematics of Computation. 5 indexed citations
8.
Hong, Qingguo, et al.. (2021). New discontinuous Galerkin algorithms and analysis for linear elasticity with symmetric stress tensor. Numerische Mathematik. 149(3). 645–678.
9.
Hong, Qingguo & Jinchao Xu. (2020). Uniform Stability and Error Analysis for Some Discontinuous Galerkin Methods. Journal of Computational Mathematics. 39(2). 283–310. 5 indexed citations
10.
Hong, Qingguo, et al.. (2020). Parameter-Robust Convergence Analysis of Fixed-Stress Split Iterative Method for Multiple-Permeability Poroelasticity Systems. Multiscale Modeling and Simulation. 18(2). 916–941. 14 indexed citations
11.
Chen, Shuangshuang, Qingguo Hong, Jinchao Xu, & Kai Yang. (2020). Robust block preconditioners for poroelasticity. Computer Methods in Applied Mechanics and Engineering. 369. 113229–113229. 8 indexed citations
12.
Hong, Qingguo, Shuonan Wu, & Jinchao Xu. (2020). An extended Galerkin analysis for elliptic problems. Science China Mathematics. 64(9). 2141–2158. 7 indexed citations
13.
Hong, Qingguo, et al.. (2020). Parameter-robust Uzawa-type iterative methods for double saddle point problems arising in Biot’s consolidation and multiple-network poroelasticity models. Mathematical Models and Methods in Applied Sciences. 30(13). 2523–2555. 13 indexed citations
14.
Hong, Qingguo, et al.. (2019). Conservative discretizations and parameter‐robust preconditioners for Biot and multiple‐network flux‐based poroelasticity models. Numerical Linear Algebra with Applications. 26(4). 26 indexed citations
15.
Wang, Wansheng & Qingguo Hong. (2019). Two-grid economical algorithms for parabolic integro-differential equations with nonlinear memory. Applied Numerical Mathematics. 142. 28–46. 17 indexed citations
16.
Hong, Qingguo & Johannes Kraus. (2018). Parameter-robust stability of classical three-field formulation of Biot's consolidation model. ETNA - Electronic Transactions on Numerical Analysis. 48. 202–226. 50 indexed citations
17.
Hong, Qingguo, Fei Wang, Shuonan Wu, & Jinchao Xu. (2018). A unified study of continuous and discontinuous Galerkin methods. Science China Mathematics. 62(1). 1–32. 65 indexed citations
18.
Hong, Qingguo & Johannes Kraus. (2016). Uniformly Stable Discontinuous Galerkin Discretization and Robust Iterative Solution Methods for the Brinkman Problem. SIAM Journal on Numerical Analysis. 54(5). 2750–2774. 19 indexed citations
19.
Hong, Qingguo, Johannes Kraus, Jinchao Xu, & Ludmil Zikatanov. (2015). A robust multigrid method for discontinuous Galerkin discretizations of Stokes and linear elasticity equations. Numerische Mathematik. 132(1). 23–49. 36 indexed citations
20.
Hong, Qingguo. (2012). A Discontinuous Galerkin Method for the Fourth-Order Curl Problem. Journal of Computational Mathematics. 30(6). 565–578. 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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