Stefan Klus
Impact in
- Computational Mathematics top 5%
-
- Model Reduction and Neural Networks
Papers in
-
- Model Reduction and Neural Networks 19
- Complex Network Analysis Techniques 3
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- Neural Networks and Applications 5
- Gaussian Processes and Bayesian Inference 5
- Co-authors
- Sebastian Peitz (2 shared papers)Feliks Nüske (4 shared papers)Christof Schütte (9 shared papers)Cecilia Clementi (1 shared paper)Péter Koltai (2 shared papers)Ingmar Schuster (3 shared papers)Krikamol Muandet (2 shared papers)Frank Noé (1 shared paper)
- Journals
- Physica D Nonlinear Phenomena (4 papers)Journal of Nonlinear Science (3 papers)The Journal of Chemical Physics (2 papers)Acta Numerica (1 paper)Journal of Computational Physics (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Stefan Klus
27 papers receiving 679 citations
Peers
Comparison fields: 5 of 73
- Computational Mathematics 29
- Statistical and Nonlinear Physics 463
- Statistics, Probability and Uncertainty 153
- Computational Mechanics 139
- Control and Systems Engineering 140
Countries citing papers authored by Stefan Klus
This map shows the geographic impact of Stefan Klus'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 Stefan Klus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Klus more than expected).
Fields of papers citing papers by Stefan Klus
This network shows the impact of papers produced by Stefan Klus. 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 Stefan Klus. The network helps show where Stefan Klus may publish in the future.
Co-authors
The 23 scholars most cited alongside Stefan Klus, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 182 | |
| 2 | 2019 | 131 | |
| 3 | 2017 | 92 | |
| 4 | 2019 | 59 | |
| 5 | 2020 | 36 | |
| 6 | 2017 | 35 | |
| 7 | 2019 | 27 | |
| 8 | 2023 | 22 | |
| 9 | 2019 | 21 | |
| 10 | 2017 | 12 | |
| 11 | 2005 | 11 | |
| 12 | 2023 | 10 | |
| 13 | 2021 | 9 | |
| 14 | 2021 | 9 | |
| 15 | 2020 | 9 | |
| 16 | 2022 | 7 | |
| 17 | 2022 | 5 | |
| 18 | 2024 | 4 | |
| 19 | 2024 | 4 | |
| 20 | 2010 | 3 |
About Stefan Klus
Stefan Klus is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Statistics, Probability and Uncertainty, Molecular Biology and Numerical Analysis, having authored 30 papers that have together received 699 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (19 papers), Probabilistic and Robust Engineering Design (8 papers), Neural Networks and Applications (5 papers), Gaussian Processes and Bayesian Inference (5 papers), Numerical methods for differential equations (4 papers), Protein Structure and Dynamics (3 papers), Tensor decomposition and applications (3 papers) and Complex Network Analysis Techniques (3 papers). The work is most often cited by research in Computational Mathematics (29 citations), Statistical and Nonlinear Physics (463 citations), Statistics, Probability and Uncertainty (153 citations), Computational Mechanics (139 citations) and Control and Systems Engineering (140 citations). Stefan Klus has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Sebastian Peitz, Feliks Nüske, Christof Schütte, Cecilia Clementi, Péter Koltai, Ingmar Schuster, Krikamol Muandet, Frank Noé, Fabian Paul and Hao Wu. Their work appears in journals such as Physica D Nonlinear Phenomena, Journal of Nonlinear Science, The Journal of Chemical Physics, Acta Numerica and Journal of Computational Physics.
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.