Yuda Chen

492 citations
22 papers · 335 · h-index 10

Impact in

Papers in

Yuda Chen

20 papers receiving 320 citations

Peers

Yuda Chen
Comparison fields: 5 of 57
  • Mechanics of Materials 130
  • General Engineering 5
  • Automotive Engineering 48
  • Computer Vision and Pattern Recognition 76
  • Mechanical Engineering 124
Replace Zijian Chen with:
Zijian Chen China
Feng Xiao China
Giuseppe Cocchetti Italy
Quang Huy Vietnam
Rims Janeliukštis Latvia
Florent Mathieu France
Tianjun Wang China
Bolin Ma China
Patrick T. Brewick United States
J. Raja India
Yuda Chen relative to Zijian Chen China Zijian Chen's profile →
Citations per field
00.5×3.7×
Zijian Chen · 1×
Citations per year

Countries citing papers authored by Yuda Chen

Since Specialization
Citations

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

Fields of papers citing papers by Yuda Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020108
2 202243
3 202035
4 201828
5 201919
6 202414
7 202313
8 202213
9 20229
10 20229
11 20207
12 20226
13 20216
14 20235
15 20235
16 20225
17 20214
18 20213
19 20192
20 20201

About Yuda Chen

Yuda Chen is a scholar working on Mechanics of Materials, Mechanical Engineering, Materials Chemistry, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 22 papers that have together received 335 indexed citations. Recurring topics across this work include Metal Alloys Wear and Properties (7 papers), Mechanical stress and fatigue analysis (4 papers), Railway Engineering and Dynamics (4 papers), Robotics and Sensor-Based Localization (3 papers), Advanced Neural Network Applications (3 papers), Rock Mechanics and Modeling (3 papers), Microstructure and Mechanical Properties of Steels (3 papers) and Seismic Waves and Analysis (2 papers). The work is most often cited by research in Mechanics of Materials (130 citations), General Engineering (5 citations), Automotive Engineering (48 citations), Computer Vision and Pattern Recognition (76 citations) and Mechanical Engineering (124 citations). Yuda Chen has collaborated with scholars based in China, Singapore and France. Frequent co-authors include Ruiming Ren, Xiujuan Zhao, Ramanpreet Singh Pahwa, Vijay Chandrasekhar, Quang-Hieu Pham, Jie Lin, Armin Mustafa, Diyuan Li, Pingkuang Luo and Quanqi Zhu. Their work appears in journals such as Wear, Applied Sciences, Journal of Alloys and Compounds, Rock Mechanics and Rock Engineering and Engineering Fracture Mechanics.

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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