Feliks Nüske
- Statistical and Nonlinear Physics top 2%
- Molecular Biology
- Materials Chemistry
- Statistics, Probability and Uncertainty top 2%
- Artificial Intelligence top 10%
- Co-authors
- Frank NoéStefan KlusCecilia ClementiSebastian PeitzAntonia S. J. S. MeyBettina G. KellerGuillermo Pérez‐HernándezChristof Schütte
- Topics
- Model Reduction and Neural Networks (12 papers)Protein Structure and Dynamics (11 papers)Probabilistic and Robust Engineering Design (8 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mathematics
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Feliks Nüske
18 papers receiving 732 citations
Peers
Comparison fields: 5 of 77
- Statistical and Nonlinear Physics 370
- Molecular Biology 318
- Materials Chemistry 142
- Statistics, Probability and Uncertainty 123
- Artificial Intelligence 104
Countries citing papers authored by Feliks Nüske
This map shows the geographic impact of Feliks Nüske'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 Feliks Nüske with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feliks Nüske more than expected).
Fields of papers citing papers by Feliks Nüske
This network shows the impact of papers produced by Feliks Nüske. 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 Feliks Nüske. The network helps show where Feliks Nüske may publish in the future.
Co-authorship network of co-authors of Feliks Nüske
This figure shows the co-authorship network connecting the top 25 collaborators of Feliks Nüske. A scholar is included among the top collaborators of Feliks Nüske 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 Feliks Nüske. Feliks Nüske 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 | 2 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 15 | |
| 8 | 9 | |
| 9 | 19 | |
| 10 | 11 | |
| 11 | 42 | |
| 12 | 4 | |
| 13 | 7 | |
| 14 | 35 | |
| 15 | 173 | |
| 16 | 37 | |
| 17 | 21 | |
| 18 | 46 | |
| 19 | 90 | |
| 20 | 205 |
About Feliks Nüske
Feliks Nüske is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Statistics, Probability and Uncertainty, having authored 21 papers that have together received 762 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (12 papers), Protein Structure and Dynamics (11 papers) and Probabilistic and Robust Engineering Design (8 papers). The work is most often cited by research in Statistical and Nonlinear Physics (370 citations), Statistics, Probability and Uncertainty (123 citations) and Computational Mathematics (8 citations). Feliks Nüske has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Frank Noé, Stefan Klus, Cecilia Clementi, Sebastian Peitz, Antonia S. J. S. Mey, Bettina G. Keller, Guillermo Pérez‐Hernández, Christof Schütte, Hao Wu and Friedrich Philipp. Their work appears in journals such as The Journal of Chemical Physics, Automatica and Journal of Chemical Theory and Computation.
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.