Jun‐Min Wang

2.5k citations
161 papers · 1.8k · h-index 23

Impact in

Papers in

Jun‐Min Wang

136 papers receiving 1.7k citations

Peers

Jun‐Min Wang
Comparison fields: 5 of 52
  • Control and Systems Engineering 1.5k
  • Computational Theory and Mathematics 803
  • Mathematical Physics 449
  • Numerical Analysis 266
  • Modeling and Simulation 77
Replace Kirsten Morris with:
Kirsten Morris Canada
Nasser‐eddine Tatar Saudi Arabia
Pavel Drábek Czechia
Jaime E. Muñoz Rivera Brazil
Salim A. Messaoudi Saudi Arabia
A. Manitius United States
Shu‐Xia Tang United States
Hua‐Cheng Zhou China
Yuan Lin Canada
Witold Respondek France
Jun‐Min Wang relative to Kirsten Morris Canada Kirsten Morris's profile →
Citations per field
00.5×3.1×
Kirsten Morris · 1×
Citations per year

Countries citing papers authored by Jun‐Min Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jun‐Min Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun‐Min Wang. 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 Jun‐Min Wang. The network helps show where Jun‐Min Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun‐Min Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun‐Min Wang Line = papers co-authored together Jun‐Min Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 161 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014194
2 2005101
3 201378
4 200855
5 200648
6 201643
7 201742
8 200541
9 201140
10 201839
11 201936
12 200532
13 201930
14 201530
15 201728
16 201926
17 201526
18 200725
19 201324
20 201823

About Jun‐Min Wang

Jun‐Min Wang is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics, Mathematical Physics, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 161 papers that have together received 1.8k indexed citations. Recurring topics across this work include Stability and Controllability of Differential Equations (119 papers), Advanced Mathematical Modeling in Engineering (80 papers), Advanced Mathematical Physics Problems (48 papers), Nonlinear Dynamics and Pattern Formation (37 papers), Numerical methods for differential equations (22 papers), Numerical methods in inverse problems (13 papers), Vibration and Dynamic Analysis (11 papers) and Model Reduction and Neural Networks (11 papers). The work is most often cited by research in Control and Systems Engineering (1.5k citations), Computational Theory and Mathematics (803 citations), Mathematical Physics (449 citations), Numerical Analysis (266 citations) and Modeling and Simulation (77 citations). Jun‐Min Wang has collaborated with scholars based in China, United States and South Africa. Frequent co-authors include Bao‐Zhu Guo, Jun‐Jun Liu, Beibei Ren, Miroslav Krstić, Genqi Xu, Jinhao Chen, Yaping Guo, Jun‐Wei Wang, Yulong Zhang and Boumediène Chentouf. Their work appears in journals such as Systems & Control Letters, IEEE Transactions on Automatic Control, Journal of Mathematical Analysis and Applications, International Journal of Control and Automatica.

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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