Tomas Sauer
Impact in
- Computational Mechanics top 5%
- Advanced Numerical Analysis Techniques
-
- Image and Signal Denoising Methods
Papers in
-
- Advanced Numerical Analysis Techniques 27
-
- Image and Signal Denoising Methods 13
- Co-authors
- Jean‐Louis Merrien (5 shared papers)Mariantonia Cotronei (15 shared papers)Costanza Conti (6 shared papers)Achim Langenbucher (5 shared papers)Gitta Kutyniok (1 shared paper)Vitalii Naumov (1 shared paper)Rosario Fedele (1 shared paper)Filippo Giammaria Praticò (1 shared paper)
In The Last Decade
Tomas Sauer
58 papers receiving 421 citations
Peers
Comparison fields: 5 of 70
- Computational Mechanics 210
- Computer Vision and Pattern Recognition 95
- Computational Theory and Mathematics 72
- Applied Mathematics 38
- Numerical Analysis 19
Countries citing papers authored by Tomas Sauer
This map shows the geographic impact of Tomas Sauer'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 Tomas Sauer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomas Sauer more than expected).
Fields of papers citing papers by Tomas Sauer
This network shows the impact of papers produced by Tomas Sauer. 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 Tomas Sauer. The network helps show where Tomas Sauer may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomas Sauer, 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 62 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 47 | |
| 2 | 2009 | 29 | |
| 3 | 2011 | 28 | |
| 4 | 2016 | 21 | |
| 5 | 2018 | 19 | |
| 6 | Stationary vector subdivision-quotient ideals, differences and approximation power | 2002 | 17 |
| 7 | 2004 | 17 | |
| 8 | 2011 | 15 | |
| 9 | 2019 | 14 | |
| 10 | 2016 | 14 | |
| 11 | 2007 | 14 | |
| 12 | 2017 | 13 | |
| 13 | 2018 | 12 | |
| 14 | 2003 | 12 | |
| 15 | 2011 | 12 | |
| 16 | 2010 | 11 | |
| 17 | 2014 | 9 | |
| 18 | 2009 | 9 | |
| 19 | 2002 | 8 | |
| 20 | 2002 | 8 |
About Tomas Sauer
Tomas Sauer is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Mechanical Engineering and Signal Processing, having authored 62 papers that have together received 433 indexed citations. Recurring topics across this work include Advanced Numerical Analysis Techniques (27 papers), Polynomial and algebraic computation (14 papers), Image and Signal Denoising Methods (13 papers), Digital Filter Design and Implementation (6 papers), Tribology and Lubrication Engineering (6 papers), Commutative Algebra and Its Applications (4 papers), Advanced Measurement and Metrology Techniques (4 papers) and Advanced machining processes and optimization (4 papers). The work is most often cited by research in Computational Mechanics (210 citations), Computer Vision and Pattern Recognition (95 citations), Computational Theory and Mathematics (72 citations), Applied Mathematics (38 citations) and Numerical Analysis (19 citations). Tomas Sauer has collaborated with scholars based in Germany, Italy and Spain. Frequent co-authors include Jean‐Louis Merrien, Mariantonia Cotronei, Costanza Conti, Achim Langenbucher, Gitta Kutyniok, Vitalii Naumov, Rosario Fedele, Filippo Giammaria Praticò, Berthold Seitz and J.M. Peña. Their work appears in journals such as Journal of Computational and Applied Mathematics, Journal of Approximation Theory, Advances in Computational Mathematics, BIT Numerical Mathematics and Constructive Approximation.
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.