Tong Qin

590 citations
11 papers · 387 indexed · h-index 7
Topics
Model Reduction and Neural Networks (4 papers)Advanced Numerical Methods in Computational Mathematics (3 papers)Numerical methods for differential equations (3 papers)
Partner nations
United StatesChinaChile

In The Last Decade

Tong Qin

10 papers receiving 374 citations

Peers

Tong Qin
Comparison fields: 5 of 59
  • Statistical and Nonlinear Physics 203
  • Computational Mechanics 165
  • Statistics, Probability and Uncertainty 82
  • Artificial Intelligence 68
  • Numerical Analysis 52
Replace Yeonjong Shin with:
Yeonjong Shin United States
Alessandro Alla Italy
Kookjin Lee United States
Patrick Blonigan United States
Eric Parish United States
Mario De Florio United States
Anthony Nouy France
Denis Ridzal United States
George Em Karniadakis United States
Yaohua Zang Germany
Tong Qin relative to Yeonjong Shin United States Yeonjong Shin's profile →
Citations per field
00.5×3.3×
Yeonjong Shin · 1×
Citations per year

Countries citing papers authored by Tong Qin

Since Specialization
Citations

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

Fields of papers citing papers by Tong Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tong Qin. 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 Tong Qin. The network helps show where Tong Qin may publish in the future.

Co-authorship network of co-authors of Tong Qin

This figure shows the co-authorship network connecting the top 25 collaborators of Tong Qin. A scholar is included among the top collaborators of Tong Qin 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 Tong Qin. Tong Qin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
#WorkIndexed citations
1 15
2 0
3 1
4 38
5 31
6 4
7 183
8 23
9 45
10 46
11 1

About Tong Qin

Tong Qin is a scholar working on Numerical Analysis, Statistical and Nonlinear Physics and Industrial and Manufacturing Engineering, having authored 11 papers that have together received 387 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (4 papers), Advanced Numerical Methods in Computational Mathematics (3 papers) and Numerical methods for differential equations (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (203 citations), Statistics, Probability and Uncertainty (82 citations) and Numerical Analysis (52 citations). Tong Qin has collaborated with scholars based in United States, China and Chile. Frequent co-authors include Dongbin Xiu, Kailiang Wu, Chi‐Wang Shu, John Jakeman, Ricardo Oyarzúa, Dominik Schötzau, Yang Yang, Zhe Chen, Zhen Chen and Jianzhe Shi. Their work appears in journals such as Journal of Computational Physics, Construction and Building Materials and SIAM Journal on Scientific Computing.

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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