Jing‐Mei Qiu

1.9k total citations
66 papers, 1.3k citations indexed

About

Jing‐Mei Qiu is a scholar working on Computational Mechanics, Applied Mathematics and Numerical Analysis. According to data from OpenAlex, Jing‐Mei Qiu has authored 66 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Computational Mechanics, 31 papers in Applied Mathematics and 14 papers in Numerical Analysis. Recurrent topics in Jing‐Mei Qiu's work include Computational Fluid Dynamics and Aerodynamics (46 papers), Gas Dynamics and Kinetic Theory (29 papers) and Fluid Dynamics and Turbulent Flows (24 papers). Jing‐Mei Qiu is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (46 papers), Gas Dynamics and Kinetic Theory (29 papers) and Fluid Dynamics and Turbulent Flows (24 papers). Jing‐Mei Qiu collaborates with scholars based in United States, China and Italy. Jing‐Mei Qiu's co-authors include Chi‐Wang Shu, Andrew Christlieb, Tao Xiong, Zhengfu Xu, Wei Guo, Giovanni Russo, Fengyan Li, Sebastiano Boscarino, Wei Guo and Jianxian Qiu and has published in prestigious journals such as The Astrophysical Journal, Journal of Computational Physics and Monthly Weather Review.

In The Last Decade

Jing‐Mei Qiu

63 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jing‐Mei Qiu United States 21 1.1k 551 316 118 92 66 1.3k
Huazhong Tang China 21 940 0.9× 332 0.6× 253 0.8× 120 1.0× 166 1.8× 61 1.2k
Yingda Cheng United States 19 872 0.8× 303 0.5× 429 1.4× 143 1.2× 33 0.4× 67 1.2k
Fengyan Li United States 23 1.1k 1.1× 286 0.5× 365 1.2× 108 0.9× 67 0.7× 59 1.4k
Matania Ben‐Artzi Israel 25 927 0.9× 852 1.5× 166 0.5× 189 1.6× 78 0.8× 79 1.9k
Lilia Krivodonova Canada 11 1.1k 1.0× 229 0.4× 269 0.9× 72 0.6× 59 0.6× 24 1.1k
Jeffrey W. Banks United States 21 801 0.8× 186 0.3× 170 0.5× 70 0.6× 28 0.3× 75 1.2k
Raphaël Loubère France 24 1.7k 1.6× 514 0.9× 174 0.6× 43 0.4× 99 1.1× 65 1.8k
Dietmar Kröner Germany 16 778 0.7× 362 0.7× 108 0.3× 46 0.4× 73 0.8× 34 1.0k
Magnus Svärd Norway 20 1.6k 1.6× 314 0.6× 436 1.4× 140 1.2× 110 1.2× 55 1.9k
Steve Shkoller United States 29 1.1k 1.1× 1.3k 2.3× 503 1.6× 511 4.3× 68 0.7× 67 2.5k

Countries citing papers authored by Jing‐Mei Qiu

Since Specialization
Citations

This map shows the geographic impact of Jing‐Mei Qiu'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 Jing‐Mei Qiu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing‐Mei Qiu more than expected).

Fields of papers citing papers by Jing‐Mei Qiu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing‐Mei Qiu. 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 Jing‐Mei Qiu. The network helps show where Jing‐Mei Qiu may publish in the future.

Co-authorship network of co-authors of Jing‐Mei Qiu

This figure shows the co-authorship network connecting the top 25 collaborators of Jing‐Mei Qiu. A scholar is included among the top collaborators of Jing‐Mei Qiu 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 Jing‐Mei Qiu. Jing‐Mei Qiu 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.
Sands, William A., Wei Guo, Jing‐Mei Qiu, & Tao Xiong. (2025). High-Order Adaptive Rank Integrators for Multiscale Linear Kinetic Transport Equations in the Hierarchical Tucker Format. SIAM Journal on Scientific Computing. 47(6). A3383–A3412.
2.
Einkemmer, Lukas, Katharina Kormann, Jonas Kusch, Ryan G. McClarren, & Jing‐Mei Qiu. (2025). A review of low-rank methods for time-dependent kinetic simulations. Journal of Computational Physics. 538. 114191–114191. 4 indexed citations
3.
Taitano, William, et al.. (2024). Krylov-based adaptive-rank implicit time integrators for stiff problems with application to nonlinear Fokker-Planck kinetic models. Journal of Computational Physics. 518. 113332–113332. 5 indexed citations
4.
5.
Guo, Wei & Jing‐Mei Qiu. (2024). A Conservative Low Rank Tensor Method for the Vlasov Dynamics. SIAM Journal on Scientific Computing. 46(1). A232–A263. 11 indexed citations
6.
Guo, Wei & Jing‐Mei Qiu. (2024). A Local Macroscopic Conservative (LoMaC) Low Rank Tensor Method for the Vlasov Dynamics. Journal of Scientific Computing. 101(3). 3 indexed citations
7.
Qiu, Jing‐Mei, et al.. (2024). A Conservative Eulerian–Lagrangian Runge–Kutta Discontinuous Galerkin Method for Linear Hyperbolic System with Large Time Stepping. Journal of Scientific Computing. 98(3). 1 indexed citations
8.
Qiu, Jing‐Mei, et al.. (2022). A fourth-order conservative semi-Lagrangian finite volume WENO scheme without operator splitting for kinetic and fluid simulations. Computer Methods in Applied Mechanics and Engineering. 395. 114973–114973. 6 indexed citations
9.
Li, Linjin, Jing‐Mei Qiu, & Giovanni Russo. (2022). A High-Order Semi-Lagrangian Finite Difference Method for Nonlinear Vlasov and BGK Models. Communications on Applied Mathematics and Computation. 5(1). 170–198. 4 indexed citations
10.
Qiu, Jing‐Mei, et al.. (2022). A generalized Eulerian-Lagrangian discontinuous Galerkin method for transport problems. Journal of Computational Physics. 464. 111160–111160. 2 indexed citations
11.
Cheng, Yingda, et al.. (2020). Stability-enhanced AP IMEX-LDG schemes for linear kinetic transport equations under a diffusive scaling. Journal of Computational Physics. 415. 109485–109485. 18 indexed citations
12.
Boscarino, Sebastiano, et al.. (2020). High order semi-Lagrangian discontinuous Galerkin method coupled with Runge-Kutta exponential integrators for nonlinear Vlasov dynamics. Journal of Computational Physics. 427. 110036–110036. 14 indexed citations
13.
14.
Xiong, Tao, Giovanni Russo, & Jing‐Mei Qiu. (2018). Conservative Multi-dimensional Semi-Lagrangian Finite Difference Scheme: Stability and Applications to the Kinetic and Fluid Simulations. Journal of Scientific Computing. 79(2). 1241–1270. 20 indexed citations
15.
Xiong, Tao, Jing‐Mei Qiu, & Zhengfu Xu. (2015). High Order Maximum-Principle-Preserving Discontinuous Galerkin Method for Convection-Diffusion Equations. SIAM Journal on Scientific Computing. 37(2). A583–A608. 31 indexed citations
16.
Xiong, Tao, Jing‐Mei Qiu, Zhengfu Xu, & Andrew Christlieb. (2014). High order maximum principle preserving semi-Lagrangian finite difference WENO schemes for the Vlasov equation. Journal of Computational Physics. 273. 618–639. 39 indexed citations
17.
Xiong, Tao, Juhi Jang, Fengyan Li, & Jing‐Mei Qiu. (2014). High order asymptotic preserving nodal discontinuous Galerkin IMEX schemes for the BGK equation. Journal of Computational Physics. 284. 70–94. 18 indexed citations
18.
Christlieb, Andrew, et al.. (2014). A high order time splitting method based on integral deferred correction for semi-Lagrangian Vlasov simulations. Journal of Computational Physics. 267. 7–27. 33 indexed citations
19.
Guo, Wei, Xinghui Zhong, & Jing‐Mei Qiu. (2012). Superconvergence of discontinuous Galerkin and local discontinuous Galerkin methods: Eigen-structure analysis based on Fourier approach. Journal of Computational Physics. 235. 458–485. 48 indexed citations
20.
Qiu, Jing‐Mei & Chi‐Wang Shu. (2011). Conservative Semi-Lagrangian Finite Difference WENO Formulations with Applications to the Vlasov Equation. Communications in Computational Physics. 10(4). 979–1000. 49 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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