David Jin
Impact in
-
- Model Reduction and Neural Networks
-
- Spreadsheets and End-User Computing
- Software Reliability and Analysis Research
Papers in
-
- Multi-Agent Systems and Negotiation 1
- Neural Networks and Applications 1
- Co-authors
- Kamyar Azizzadenesheli (1 shared paper)Burigede Liu (1 shared paper)Hongkai Zheng (1 shared paper)Anima Anandkumar (1 shared paper)Nikola Kovachki (1 shared paper)Zongyi Li (1 shared paper)Gregg Rothermel (1 shared paper)Margaret Burnett (1 shared paper)
- Journals
- JAMA Network Open (1 paper)Blood (1 paper)Journal of the Association for Information Systems (1 paper)Applied Mechanics and Materials (4 papers)Advances in intelligent and soft computing (4 papers)
- Partner nations
- United StatesGermanyChina
In The Last Decade
David Jin
28 papers receiving 331 citations
David Jin's Hit Papers
Peers
Comparison fields: 5 of 113
- Statistical and Nonlinear Physics 56
- Software 19
- Artificial Intelligence 68
- Computer Science Applications 10
- Computational Mechanics 29
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 28 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 | 122 |
| 2 | 2012 | 63 | |
| 3 | 2012 | 55 | |
| 4 | 2011 | 22 | |
| 5 | 2003 | 19 | |
| 6 | 2023 | 17 | |
| 7 | 2012 | 11 | |
| 8 | 2022 | 4 | |
| 9 | 2012 | 3 | |
| 10 | 2011 | 2 | |
| 11 | 2012 | 2 | |
| 12 | 2023 | 2 | |
| 13 | 2013 | 2 | |
| 14 | 2013 | 2 | |
| 15 | 2024 | 1 | |
| 16 | 2013 | 1 | |
| 17 | 2013 | 1 | |
| 18 | 2011 | 1 | |
| 19 | 2011 | 1 | |
| 20 | 2014 | 1 |
About David Jin
David Jin is a scholar working on Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics, Animal Science and Zoology and Architecture, having authored 28 papers that have together received 340 indexed citations. Recurring topics across this work include Spreadsheets and End-User Computing (1 paper), Multi-Agent Systems and Negotiation (1 paper), Architecture and Computational Design (1 paper), Physical Activity and Health (1 paper), Teaching and Learning Programming (1 paper), Model Reduction and Neural Networks (1 paper), Educational Games and Gamification (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (56 citations), Software (19 citations), Artificial Intelligence (68 citations), Computer Science Applications (10 citations) and Computational Mechanics (29 citations). David Jin has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Kamyar Azizzadenesheli, Burigede Liu, Hongkai Zheng, Anima Anandkumar, Nikola Kovachki, Zongyi Li, Gregg Rothermel, Margaret Burnett, Xin Zhao and Aiden Doherty. Their work appears in journals such as JAMA Network Open, Blood, Journal of the Association for Information Systems, Applied Mechanics and Materials and Advances in intelligent and soft computing.
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.