W. Guan

13.2k citations
58 papers · 329 · h-index 10

Impact in

Papers in

W. Guan

50 papers receiving 318 citations

Peers

W. Guan
Comparison fields: 5 of 64
  • Applied Mathematics 141
  • Numerical Analysis 39
  • Computational Theory and Mathematics 85
  • Information Systems and Management 34
  • Mathematical Physics 41
Replace Jean Della Dora with:
Jean Della Dora France
Simon Plouffe Canada
Tewodros Amdeberhan United States
G. L. Litvinov Russia
Duane W. DeTemple United States
Sinai Robins United States
P. B. Borwein Canada
L. Pasquini Italy
Suk-Geun Hwang South Korea
Yifeng Xue China
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Citations per field
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Citations per year

Countries citing papers authored by W. Guan

Since Specialization
Citations

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

Fields of papers citing papers by W. Guan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside W. Guan, 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 W. Guan Line = papers co-authored together W. Guan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202156
2 200739
3 201938
4 201519
5 201919
6 202318
7 201912
8 201811
9 201510
10 20239
11 20248
12 20176
13 20196
14 20174
15 20214
16 20164
17 20194
18
Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
20193
19 20193
20 20203

About W. Guan

W. Guan is a scholar working on Applied Mathematics, Computer Networks and Communications, Nuclear and High Energy Physics, Numerical Analysis and Information Systems and Management, having authored 58 papers that have together received 329 indexed citations. Recurring topics across this work include Nonlinear Differential Equations Analysis (20 papers), Distributed and Parallel Computing Systems (19 papers), Nonlinear Partial Differential Equations (16 papers), Differential Equations and Numerical Methods (12 papers), Advanced Mathematical Modeling in Engineering (11 papers), Scientific Computing and Data Management (11 papers), Particle physics theoretical and experimental studies (10 papers) and Particle Detector Development and Performance (10 papers). The work is most often cited by research in Applied Mathematics (141 citations), Numerical Analysis (39 citations), Computational Theory and Mathematics (85 citations), Information Systems and Management (34 citations) and Mathematical Physics (41 citations). W. Guan has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Da-Bin Wang, T. Wenaus, Federico Carminati, C. Zhou, S. Sun, T. Maeno, S. L. Wu, Alberto Di Meglio, Miron Livny and J. Chan. Their work appears in journals such as Advances in Difference Equations, Computers & Mathematics with Applications, Gels, Applied Mathematics & Optimization and Boundary Value Problems.

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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