Jonathan W. Siegel
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 5%
- Computational Mechanics top 10%
- Computer Vision and Pattern Recognition
- Mechanics of Materials
- Co-authors
- Jinchao XuZi‐Kui LiuQingguo HongWenrui HaoHeinz EulauRobert D. NowakRussel E. CaflischStephan Wojtowytsch
- Topics
- Neural Networks and Applications (9 papers)Model Reduction and Neural Networks (7 papers)Machine Learning in Materials Science (4 papers)
- Partner nations
- United StatesChina
In The Last Decade
Jonathan W. Siegel
15 papers receiving 233 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 109
- Statistical and Nonlinear Physics 103
- Computational Mechanics 68
- Computer Vision and Pattern Recognition 40
- Mechanics of Materials 32
Countries citing papers authored by Jonathan W. Siegel
This map shows the geographic impact of Jonathan W. Siegel'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 Jonathan W. Siegel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan W. Siegel more than expected).
Fields of papers citing papers by Jonathan W. Siegel
This network shows the impact of papers produced by Jonathan W. Siegel. 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 Jonathan W. Siegel. The network helps show where Jonathan W. Siegel may publish in the future.
Co-authorship network of co-authors of Jonathan W. Siegel
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan W. Siegel. A scholar is included among the top collaborators of Jonathan W. Siegel 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 Jonathan W. Siegel. Jonathan W. Siegel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 8 | |
| 9 | 28 | |
| 10 | 15 | |
| 11 | 13 | |
| 12 | 7 | |
| 13 | 23 | |
| 14 | 30 | |
| 15 | Improved Approximation Properties of Dictionaries and Applications to Neural Networks | 2 |
| 16 | 25 | |
| 17 | 69 | |
| 18 | 5 | |
| 19 | 0 | |
| 20 | Accelerated First-Order Optimization with Orthogonality Constraints | 0 |
About Jonathan W. Siegel
Jonathan W. Siegel is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computational Mechanics, having authored 21 papers that have together received 234 indexed citations. Recurring topics across this work include Neural Networks and Applications (9 papers), Model Reduction and Neural Networks (7 papers) and Machine Learning in Materials Science (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (103 citations), Artificial Intelligence (109 citations) and Computational Mechanics (68 citations). Jonathan W. Siegel has collaborated with scholars based in United States and China. Frequent co-authors include Jinchao Xu, Jinchao Xu, Zi‐Kui Liu, Qingguo Hong, Wenrui Hao, Heinz Eulau, Robert D. Nowak, Russel E. Caflisch, Stephan Wojtowytsch and Ronald DeVore. Their work appears in journals such as Journal of Computational Physics, IEEE Transactions on Information Theory and Neural Networks.
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.