Kun Wang

104 papers receiving 997 citations

Kun Wang's Hit Papers

Transfer learning framework for the wind pressure prediction of high-rise building surfaces using wind tunnel experiments and machine learning 2025 · 35 citations
350Years since publication102030

Peers

Kun Wang
Comparison fields: 5 of 136
  • Biochemistry 38
  • Cancer Research 55
  • Genetics 107
  • Food Science 69
  • Pharmacology 31
Replace Xiaoyue Yang with:
Xiaoyue Yang China
David Allaway United Kingdom
Mengli Wang China
Lan Ye China
Jungyeon Kim South Korea
Mónika Varga Hungary
Lifeng Wang China
Yanli Gao China
Muqing Zhang China
Kun Wang relative to Xiaoyue Yang China Xiaoyue Yang's profile →
Citations per field
00.5×2.7×
Xiaoyue Yang · 1×
Citations per year

Countries citing papers authored by Kun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Kun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 128 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015195
2 201787
3 201448
4
Transfer learning framework for the wind pressure prediction of high-rise building surfaces using wind tunnel experiments and machine learning
Hit paper breakdown →
202535
5 202432
6 202328
7 202424
8 202421
9 202220
10 202420
11 202419
12 202419
13 202018
14 202316
15 201714
16 201213
17 201913
18 202413
19 202412
20 202412

About Kun Wang

Kun Wang is a scholar working on Molecular Biology, Materials Chemistry, Electrical and Electronic Engineering, Biomedical Engineering and Renewable Energy, Sustainability and the Environment, having authored 128 papers that have together received 1.0k indexed citations. Recurring topics across this work include Catalytic Processes in Materials Science (5 papers), Advanced Photocatalysis Techniques (4 papers), Wind and Air Flow Studies (4 papers), Electrocatalysts for Energy Conversion (4 papers), Neural Networks and Reservoir Computing (4 papers), Metal-Organic Frameworks: Synthesis and Applications (3 papers), Fuel Cells and Related Materials (3 papers) and Supercapacitor Materials and Fabrication (3 papers). The work is most often cited by research in Biochemistry (38 citations), Cancer Research (55 citations), Genetics (107 citations), Food Science (69 citations) and Pharmacology (31 citations). Kun Wang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Meng Chen, Kunde Lin, Xinwen Huang, Nan Li, Junling Shi, Ruijun Long, Qiang Qiu, Tao Ma, Zhengqiang Ni and Richard J. Abbott. Their work appears in journals such as International Journal of Biological Macromolecules, Journal of environmental chemical engineering, Journal of Building Engineering, Journal of Agricultural and Food Chemistry and Journal of Photochemistry and Photobiology B Biology.

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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