Pengzhan Jin

32 total papers · 571 total citations
9 papers, 278 citations indexed

About

Pengzhan Jin is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mathematics. According to data from OpenAlex, Pengzhan Jin has authored 9 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 2 papers in Computational Mathematics. Recurrent topics in Pengzhan Jin's work include Model Reduction and Neural Networks (4 papers), Neural Networks and Applications (4 papers) and Advanced Numerical Methods in Computational Mathematics (2 papers). Pengzhan Jin is often cited by papers focused on Model Reduction and Neural Networks (4 papers), Neural Networks and Applications (4 papers) and Advanced Numerical Methods in Computational Mathematics (2 papers). Pengzhan Jin collaborates with scholars based in China and United States. Pengzhan Jin's co-authors include Lu Lu, Shuai Meng, George Em Karniadakis, Yifa Tang, Zhen Zhang, Ioannis G. Kevrekidis and Bo Xiao and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks and SIAM Journal on Scientific Computing.

In The Last Decade

Pengzhan Jin

9 papers receiving 272 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Pengzhan Jin 192 101 49 37 33 9 278
Hunter Johnston 142 0.7× 44 0.4× 59 1.2× 61 1.6× 36 1.1× 13 307
Carl Leake 122 0.6× 42 0.4× 51 1.0× 45 1.2× 31 0.9× 14 251
Yaohua Zang 186 1.0× 43 0.4× 81 1.7× 17 0.5× 20 0.6× 6 251
Jakob Zech 128 0.7× 80 0.8× 94 1.9× 55 1.5× 25 0.8× 20 293
David Widemann 114 0.6× 49 0.5× 52 1.1× 15 0.4× 15 0.5× 10 232
Chad Lieberman 126 0.7× 51 0.5× 39 0.8× 43 1.2× 24 0.7× 9 271
David Sondak 181 0.9× 59 0.6× 146 3.0× 11 0.3× 17 0.5× 21 311
Jonathan W. Siegel 97 0.5× 107 1.1× 66 1.3× 9 0.2× 17 0.5× 19 227
Christian Moya 102 0.5× 67 0.7× 37 0.8× 10 0.3× 57 1.7× 23 207
Levi D. McClenny 179 0.9× 58 0.6× 63 1.3× 6 0.2× 35 1.1× 7 324

Countries citing papers authored by Pengzhan Jin

Since Specialization
Citations

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

Fields of papers citing papers by Pengzhan Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pengzhan Jin

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

All Works

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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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