Travis Askham
Impact in
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- Model Reduction and Neural Networks
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- Probabilistic and Robust Engineering Design
Papers in
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- Electromagnetic Scattering and Analysis 3
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- Model Reduction and Neural Networks 2
- Nonlinear Photonic Systems 1
- Co-authors
- J. Nathan Kutz (4 shared papers)Steven L. Brunton (3 shared papers)Aleksandr Y. Aravkin (2 shared papers)Peng Zheng (2 shared papers)Antoine Cerfon (1 shared paper)Leslie Greengard (2 shared papers)Shidong Jiang (1 shared paper)N. Benjamin Erichson (1 shared paper)
- Journals
- Journal of Computational Physics (3 papers)Journal of the Optical Society of America B (1 paper)SIAM Review (1 paper)SIAM Journal on Applied Dynamical Systems (1 paper)IEEE Access (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Travis Askham
12 papers receiving 282 citations
Peers
Comparison fields: 5 of 53
- Statistical and Nonlinear Physics 151
- Statistics, Probability and Uncertainty 53
- Computational Mechanics 87
- Control and Systems Engineering 61
- Artificial Intelligence 48
Countries citing papers authored by Travis Askham
This map shows the geographic impact of Travis Askham'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 Travis Askham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Travis Askham more than expected).
Fields of papers citing papers by Travis Askham
This network shows the impact of papers produced by Travis Askham. 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 Travis Askham. The network helps show where Travis Askham may publish in the future.
Co-authors
The 10 scholars most cited alongside Travis Askham, 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 | 2018 | 132 | |
| 2 | 2018 | 100 | |
| 3 | 2017 | 25 | |
| 4 | 2020 | 13 | |
| 5 | 2017 | 9 | |
| 6 | 2022 | 8 | |
| 7 | 2017 | 3 | |
| 8 | Sparse Relaxed Regularized Regression: SR3. | 2018 | 2 |
| 9 | 2018 | 2 | |
| 10 | 2014 | 1 | |
| 11 | 2017 | 1 | |
| 12 | 2017 | 1 | |
| 13 | 2023 | 0 |
About Travis Askham
Travis Askham is a scholar working on Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics, Ocean Engineering, Mathematical Physics and Computational Mechanics, having authored 13 papers that have together received 297 indexed citations. Recurring topics across this work include Electromagnetic Scattering and Analysis (3 papers), Geophysical Methods and Applications (3 papers), Probabilistic and Robust Engineering Design (2 papers), Model Reduction and Neural Networks (2 papers), Numerical methods in inverse problems (2 papers), Microwave Imaging and Scattering Analysis (2 papers), Nonlinear Photonic Systems (1 paper) and Numerical methods for differential equations (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (151 citations), Statistics, Probability and Uncertainty (53 citations), Computational Mechanics (87 citations), Control and Systems Engineering (61 citations) and Artificial Intelligence (48 citations). Travis Askham has collaborated with scholars based in United States and Canada. Frequent co-authors include J. Nathan Kutz, Steven L. Brunton, Aleksandr Y. Aravkin, Peng Zheng, Antoine Cerfon, Leslie Greengard, Shidong Jiang, N. Benjamin Erichson, Carlos F. Borges and Manas Rachh. Their work appears in journals such as Journal of Computational Physics, Journal of the Optical Society of America B, SIAM Review, SIAM Journal on Applied Dynamical Systems and IEEE Access.
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.