Jon Jacobsen
Impact in
- Modeling and Simulation top 5%
- Fractional Differential Equations Solutions
- Numerical Analysis top 10%
- Differential Equations and Numerical Methods
- Iterative Methods for Nonlinear Equations
Papers in
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- Advanced Clustering Algorithms Research 2
- Advanced Graph Neural Networks 2
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- Geometric Analysis and Curvature Flows 3
- Nonlinear Partial Differential Equations 3
- Co-authors
- Klaus Schmitt (1 shared paper)Julijana Gjorgjieva (1 shared paper)Ira Assent (4 shared papers)Son T. (3 shared papers)Mark A. Lewis (1 shared paper)Sihem Amer-Yahia (2 shared papers)Yu Jin (1 shared paper)Quoc Viet Hung Nguyen (1 shared paper)
- Journals
- SIAM Journal on Applied Mathematics (1 paper)Journal of Mathematical Biology (1 paper)Data Mining and Knowledge Discovery (1 paper)SIAM Review (1 paper)Topological Methods in Nonlinear Analysis (1 paper)
- Partner nations
- United StatesDenmarkFrance
In The Last Decade
Jon Jacobsen
14 papers receiving 269 citations
Peers
Comparison fields: 5 of 59
- Modeling and Simulation 97
- Numerical Analysis 77
- Applied Mathematics 95
- Statistical and Nonlinear Physics 60
- Computational Theory and Mathematics 58
Countries citing papers authored by Jon Jacobsen
This map shows the geographic impact of Jon Jacobsen'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 Jon Jacobsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jon Jacobsen more than expected).
Fields of papers citing papers by Jon Jacobsen
This network shows the impact of papers produced by Jon Jacobsen. 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 Jon Jacobsen. The network helps show where Jon Jacobsen may publish in the future.
Co-authors
The 11 scholars most cited alongside Jon Jacobsen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 145 | |
| 2 | 1999 | 20 | |
| 3 | 2007 | 20 | |
| 4 | 2020 | 19 | |
| 5 | 2017 | 18 | |
| 6 | 2014 | 15 | |
| 7 | 2018 | 14 | |
| 8 | 2008 | 10 | |
| 9 | 2004 | 8 | |
| 10 | 2018 | 7 | |
| 11 | Approximations of Continuous Newton's Method: An Extension of Cayley's Problem | 2007 | 5 |
| 12 | 2007 | 5 | |
| 13 | Monotone Solutions of a Nonautonomous Differential Equation for a Sedimenting Sphere | 2016 | 4 |
| 14 | 2014 | 1 |
About Jon Jacobsen
Jon Jacobsen is a scholar working on Artificial Intelligence, Applied Mathematics, Mathematical Physics, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 14 papers that have together received 291 indexed citations. Recurring topics across this work include Geometric Analysis and Curvature Flows (3 papers), Nonlinear Partial Differential Equations (3 papers), Complex Network Analysis Techniques (2 papers), Data Management and Algorithms (2 papers), Mathematical and Theoretical Epidemiology and Ecology Models (2 papers), Advanced Clustering Algorithms Research (2 papers), Graph Theory and Algorithms (2 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Modeling and Simulation (97 citations), Numerical Analysis (77 citations), Applied Mathematics (95 citations), Statistical and Nonlinear Physics (60 citations) and Computational Theory and Mathematics (58 citations). Jon Jacobsen has collaborated with scholars based in United States, Denmark and France. Frequent co-authors include Klaus Schmitt, Julijana Gjorgjieva, Ira Assent, Son T., Mark A. Lewis, Sihem Amer-Yahia, Yu Jin, Quoc Viet Hung Nguyen, Robert D. Guy and Ivor Spence. Their work appears in journals such as SIAM Journal on Applied Mathematics, Journal of Mathematical Biology, Data Mining and Knowledge Discovery, SIAM Review and Topological Methods in Nonlinear Analysis.
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.