David Jin
Impact in
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- Spreadsheets and End-User Computing
- Software Reliability and Analysis Research
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- Model Reduction and Neural Networks
Papers in
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- Retinoids in leukemia and cellular processes 1
- Co-authors
- Nikola Kovachki (1 shared paper)Zongyi Li (1 shared paper)Kamyar Azizzadenesheli (1 shared paper)Anima Anandkumar (1 shared paper)Hongkai Zheng (1 shared paper)Burigede Liu (1 shared paper)Baiyu Zhang (16 shared papers)Gregg Rothermel (1 shared paper)
- Journals
- JAMA Network Open (1 paper)Journal of the Association for Information Systems (1 paper)Blood (1 paper)Applied Mechanics and Materials (4 papers)Lecture notes in electrical engineering (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
David Jin
27 papers receiving 286 citations
David Jin's Hit Papers
Peers
Comparison fields: 5 of 114
- Software 18
- Statistical and Nonlinear Physics 51
- Artificial Intelligence 60
- Computer Science Applications 8
- Computational Mechanics 27
Countries citing papers authored by David Jin
This map shows the geographic impact of David 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 David Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Jin more than expected).
Fields of papers citing papers by David Jin
This network shows the impact of papers produced by David 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 David Jin. The network helps show where David Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside David Jin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Physics-Informed Neural Operator for Learning Partial Differential Equations Hit paper breakdown → | 2024 | 103 |
| 2 | 2012 | 52 | |
| 3 | 2012 | 51 | |
| 4 | 2011 | 19 | |
| 5 | 2003 | 18 | |
| 6 | 2023 | 13 | |
| 7 | 2012 | 11 | |
| 8 | 2022 | 4 | |
| 9 | 2012 | 3 | |
| 10 | 2012 | 2 | |
| 11 | 2011 | 2 | |
| 12 | 2013 | 2 | |
| 13 | 2023 | 2 | |
| 14 | 2013 | 1 | |
| 15 | 2011 | 1 | |
| 16 | 2013 | 1 | |
| 17 | 2014 | 1 | |
| 18 | 2013 | 1 | |
| 19 | 2011 | 1 | |
| 20 | 2011 | 1 |
About David Jin
David Jin is a scholar working on Artificial Intelligence, Molecular Biology, General Health Professions, Statistical and Nonlinear Physics and Animal Science and Zoology, having authored 27 papers that have together received 296 indexed citations. Recurring topics across this work include Architecture and Computational Design (1 paper), Physical Activity and Health (1 paper), Obesity, Physical Activity, Diet (1 paper), Health and Lifestyle Studies (1 paper), Educational Games and Gamification (1 paper), Animal Virus Infections Studies (1 paper), Spreadsheets and End-User Computing (1 paper) and Retinoids in leukemia and cellular processes (1 paper). The work is most often cited by research in Software (18 citations), Statistical and Nonlinear Physics (51 citations), Artificial Intelligence (60 citations), Computer Science Applications (8 citations) and Computational Mechanics (27 citations). David Jin has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Nikola Kovachki, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Hongkai Zheng, Burigede Liu, Baiyu Zhang, Gregg Rothermel, Margaret Burnett and Xin Zhao. Their work appears in journals such as JAMA Network Open, Journal of the Association for Information Systems, Blood, Applied Mechanics and Materials and Lecture notes in electrical engineering.
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.