Jonas Kusch
Impact in
- Computational Mathematics top 2%
- Tensor decomposition and applications
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- Model Reduction and Neural Networks
Papers in
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- Computational Fluid Dynamics and Aerodynamics 7
- Advanced Numerical Methods in Computational Mathematics 7
- Sparse and Compressive Sensing Techniques 5
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- Model Reduction and Neural Networks 12
- Co-authors
- Christian Lubich (3 shared papers)Martin Frank (9 shared papers)Lukas Einkemmer (8 shared papers)Ryan G. McClarren (5 shared papers)Christian Klingenberg (1 shared paper)Jing‐Mei Qiu (2 shared papers)Christophe Matthys (1 shared paper)M. J. Frank (1 shared paper)
In The Last Decade
Jonas Kusch
24 papers receiving 203 citations
Peers
Comparison fields: 5 of 58
- Computational Mathematics 49
- Statistical and Nonlinear Physics 121
- Statistics, Probability and Uncertainty 44
- Numerical Analysis 33
- Computational Mechanics 110
Countries citing papers authored by Jonas Kusch
This map shows the geographic impact of Jonas Kusch'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 Jonas Kusch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Kusch more than expected).
Fields of papers citing papers by Jonas Kusch
This network shows the impact of papers produced by Jonas Kusch. 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 Jonas Kusch. The network helps show where Jonas Kusch may publish in the future.
Co-authors
The 17 scholars most cited alongside Jonas Kusch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 53 | |
| 2 | 2022 | 21 | |
| 3 | 2023 | 17 | |
| 4 | 2019 | 15 | |
| 5 | 2022 | 15 | |
| 6 | 2022 | 13 | |
| 7 | 2023 | 12 | |
| 8 | 2018 | 12 | |
| 9 | 2024 | 11 | |
| 10 | 2024 | 11 | |
| 11 | 2024 | 9 | |
| 12 | 2020 | 8 | |
| 13 | 2024 | 8 | |
| 14 | 2024 | 7 | |
| 15 | 2025 | 4 | |
| 16 | 2025 | 2 | |
| 17 | 2020 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2025 | 2 | |
| 20 | 2023 | 1 |
About Jonas Kusch
Jonas Kusch is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty, Computational Mathematics and Computational Theory and Mathematics, having authored 27 papers that have together received 230 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (12 papers), Probabilistic and Robust Engineering Design (8 papers), Computational Fluid Dynamics and Aerodynamics (7 papers), Advanced Numerical Methods in Computational Mathematics (7 papers), Sparse and Compressive Sensing Techniques (5 papers), Gas Dynamics and Kinetic Theory (4 papers), Tensor decomposition and applications (4 papers) and Matrix Theory and Algorithms (4 papers). The work is most often cited by research in Computational Mathematics (49 citations), Statistical and Nonlinear Physics (121 citations), Statistics, Probability and Uncertainty (44 citations), Numerical Analysis (33 citations) and Computational Mechanics (110 citations). Jonas Kusch has collaborated with scholars based in Germany, Austria and Norway. Frequent co-authors include Christian Lubich, Martin Frank, Lukas Einkemmer, Ryan G. McClarren, Christian Klingenberg, Jing‐Mei Qiu, Christophe Matthys, M. J. Frank, Katharina Kormann and Maria Vertzoni. Their work appears in journals such as Journal of Computational Physics, BIT Numerical Mathematics, SIAM Journal on Scientific Computing, Journal of Scientific Computing and ACM Transactions on Mathematical Software.
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.