John Burkardt
- Computational Mechanics top 2%
- Statistics, Probability and Uncertainty top 0.5%
- Statistical and Nonlinear Physics top 2%
- Computational Theory and Mathematics top 2%
- Numerical Analysis top 5%
- Co-authors
- Max GunzburgerWerner C. RheinboldtMichael EldredHyung‐Chun LeeJanet PetersonVicente RomeroMarcus R. GarvieMauro Perego
- Topics
- Probabilistic and Robust Engineering Design (10 papers)Advanced Numerical Methods in Computational Mathematics (9 papers)Model Reduction and Neural Networks (6 papers)
- Partner nations
- United StatesCanadaChina
In The Last Decade
John Burkardt
38 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 90
- Computational Mechanics 433
- Statistics, Probability and Uncertainty 409
- Statistical and Nonlinear Physics 335
- Computational Theory and Mathematics 318
- Numerical Analysis 223
Countries citing papers authored by John Burkardt
This map shows the geographic impact of John Burkardt'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 John Burkardt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Burkardt more than expected).
Fields of papers citing papers by John Burkardt
This network shows the impact of papers produced by John Burkardt. 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 John Burkardt. The network helps show where John Burkardt may publish in the future.
Co-authorship network of co-authors of John Burkardt
This figure shows the co-authorship network connecting the top 25 collaborators of John Burkardt. A scholar is included among the top collaborators of John Burkardt 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 John Burkardt. John Burkardt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 21 | |
| 3 | 4 | |
| 4 | 13 | |
| 5 | 2 | |
| 6 | 21 | |
| 7 | 13 | |
| 8 | 28 | |
| 9 | 5 | |
| 10 | 29 | |
| 11 | 37 | |
| 12 | 20 | |
| 13 | MATLAB Parallel Computing | 2 |
| 14 | 32 | |
| 15 | REDUCED ORDER MODELING OF SOME NONLINEAR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS | 20 |
| 16 | 10 | |
| 17 | 25 | |
| 18 | 84 | |
| 19 | ALGORITHM 596 A Program for a Locally Parameterized Continuation Process | 38 |
| 20 | A Program for a Locally-Parametrized Continuation Process. | 2 |
About John Burkardt
John Burkardt is a scholar working on Computer Graphics and Computer-Aided Design, Statistics, Probability and Uncertainty and Numerical Analysis, having authored 38 papers that have together received 1.4k indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (10 papers), Advanced Numerical Methods in Computational Mathematics (9 papers) and Model Reduction and Neural Networks (6 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (409 citations), Numerical Analysis (223 citations) and Statistical and Nonlinear Physics (335 citations). John Burkardt has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Max Gunzburger, Werner C. Rheinboldt, Michael Eldred, Hyung‐Chun Lee, Janet Peterson, Vicente Romero, Marcus R. Garvie, Mauro Perego, Clayton Webster and Hoa Nguyen. Their work appears in journals such as Physical Review B, Journal of Hydrology and Computer Methods in Applied Mechanics and Engineering.
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.