Matthias Rottmann
- Nuclear and High Energy Physics top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering
- Computational Theory and Mathematics
- Co-authors
- Hanno GottschalkRobin ChanKarsten KahlAndreas FrommerConstantia AlexandrouSimone BacchioJacob FinkenrathTilo Wettig
- Topics
- Matrix Theory and Algorithms (6 papers)Adversarial Robustness in Machine Learning (5 papers)Advanced Neural Network Applications (5 papers)
- Cited by
- Nuclear and High Energy PhysicsComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- Angewandte Chemie International EditionSHILAP Revista de lepidopterologíaJournal of Medicinal Chemistry
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Matthias Rottmann
23 papers receiving 237 citations
Peers
Comparison fields: 5 of 55
- Nuclear and High Energy Physics 84
- Artificial Intelligence 82
- Computer Vision and Pattern Recognition 79
- Automotive Engineering 15
- Computational Theory and Mathematics 15
Countries citing papers authored by Matthias Rottmann
This map shows the geographic impact of Matthias Rottmann'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 Matthias Rottmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Rottmann more than expected).
Fields of papers citing papers by Matthias Rottmann
This network shows the impact of papers produced by Matthias Rottmann. 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 Matthias Rottmann. The network helps show where Matthias Rottmann may publish in the future.
Co-authorship network of co-authors of Matthias Rottmann
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Rottmann. A scholar is included among the top collaborators of Matthias Rottmann 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 Matthias Rottmann. Matthias Rottmann 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 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 6 | |
| 9 | 2 | |
| 10 | 2 | |
| 11 | 1 | |
| 12 | 70 | |
| 13 | 3 | |
| 14 | 6 | |
| 15 | 8 | |
| 16 | 6 | |
| 17 | Adaptive Domain Decomposition Multigrid for Lattice QCD | 2 |
| 18 | 23 | |
| 19 | 15 | |
| 20 | 1 |
About Matthias Rottmann
Matthias Rottmann is a scholar working on Instrumentation, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 244 indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (6 papers), Adversarial Robustness in Machine Learning (5 papers) and Advanced Neural Network Applications (5 papers). The work is most often cited by research in Nuclear and High Energy Physics (84 citations), Computer Vision and Pattern Recognition (79 citations) and Artificial Intelligence (82 citations). Matthias Rottmann has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Hanno Gottschalk, Robin Chan, Karsten Kahl, Andreas Frommer, Constantia Alexandrou, Simone Bacchio, Jacob Finkenrath, Tilo Wettig, Peter Schlicht and Fabian Hüger. Their work appears in journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and Journal of Medicinal Chemistry.
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.