Jared Callaham

13 papers receiving 388 citations

Jared Callaham's Hit Papers

PySINDy: A comprehensive Python package for robust sparse system identification 2022 · 105 citations
1050+1+2Years since publication255075100

Peers

Jared Callaham
Comparison fields: 5 of 66
  • Statistical and Nonlinear Physics 239
  • Statistics, Probability and Uncertainty 68
  • Computational Mechanics 118
  • Control and Systems Engineering 75
  • Artificial Intelligence 85
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Countries citing papers authored by Jared Callaham

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 16 scholars most cited alongside Jared Callaham, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jared Callaham Line = papers co-authored together Jared Callaham links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
PySINDy: A comprehensive Python package for robust sparse system identification
Hit paper breakdown →
2022105
2 202166
3 202156
4 202152
5 202234
6 202229
7 202228
8 202113
9 201711
10 20234
11 20213
12 20212
13
Robust reconstruction of flow fields from limited measurements
20181

About Jared Callaham

Jared Callaham is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Statistics, Probability and Uncertainty, Condensed Matter Physics and Control and Systems Engineering, 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), Probabilistic and Robust Engineering Design (4 papers), Theoretical and Computational Physics (2 papers), Fluid Dynamics and Vibration Analysis (2 papers), Material Dynamics and Properties (1 paper), Reservoir Engineering and Simulation Methods (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (239 citations), Statistics, Probability and Uncertainty (68 citations), Computational Mechanics (118 citations), Control and Systems Engineering (75 citations) and Artificial Intelligence (85 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, Bingni W. Brunton, Brian M. de Silva and Zachary G. Nicolaou. Their work appears in journals such as Journal of Fluid Mechanics, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Nature Communications, AIAA Journal 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.

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