Jared Callaham
- Statistical and Nonlinear Physics top 5%
- Computational Mechanics top 10%
- Artificial Intelligence
- Control and Systems Engineering top 10%
- Statistics, Probability and Uncertainty top 5%
- Co-authors
- Steven L. BruntonJean-Christophe LoiseauJ. Nathan KutzAlan A. KaptanogluGeorgios RigasAleksandr Y. AravkinC. HansenBrian M. de Silva
- Topics
- Model Reduction and Neural Networks (8 papers)Fluid Dynamics and Turbulent Flows (5 papers)Probabilistic and Robust Engineering Design (4 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mechanics
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Jared Callaham
13 papers receiving 388 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Statistical and Nonlinear Physics 239
- Computational Mechanics 118
- Artificial Intelligence 85
- Control and Systems Engineering 75
- Statistics, Probability and Uncertainty 68
Countries citing papers authored by Jared Callaham
This map shows the geographic impact of Jared Callaham'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 Jared Callaham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jared Callaham more than expected).
Fields of papers citing papers by Jared Callaham
This network shows the impact of papers produced by Jared Callaham. 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 Jared Callaham. The network helps show where Jared Callaham may publish in the future.
Co-authorship network of co-authors of Jared Callaham
This figure shows the co-authorship network connecting the top 25 collaborators of Jared Callaham. A scholar is included among the top collaborators of Jared Callaham 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 Jared Callaham. Jared Callaham is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 34 | |
| 3 | PySINDy: A comprehensive Python package for robust sparse system identificationbreakdown → | 105 |
| 4 | 28 | |
| 5 | 29 | |
| 6 | 2 | |
| 7 | 52 | |
| 8 | 66 | |
| 9 | 13 | |
| 10 | 3 | |
| 11 | 56 | |
| 12 | Robust reconstruction of flow fields from limited measurements | 1 |
| 13 | 11 |
About Jared Callaham
Jared Callaham is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Computational Mechanics, having authored 13 papers that have together received 404 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (8 papers), Fluid Dynamics and Turbulent Flows (5 papers) and Probabilistic and Robust Engineering Design (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (239 citations), Statistics, Probability and Uncertainty (68 citations) and Computational Mechanics (118 citations). Jared Callaham has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Steven L. Brunton, Jean-Christophe Loiseau, J. Nathan Kutz, Alan A. Kaptanoglu, Georgios Rigas, Aleksandr Y. Aravkin, C. Hansen, Brian M. de Silva, Bingni W. Brunton and Charles B. Delahunt. Their work appears in journals such as Nature Communications, Journal of Fluid Mechanics and Science Advances.
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.