Markus Grasmair
Impact in
- Mathematical Physics top 2%
- Numerical methods in inverse problems
- Computational Mechanics top 2%
- Sparse and Compressive Sensing Techniques
Papers in
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- Numerical methods in inverse problems 19
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- Sparse and Compressive Sensing Techniques 15
- Co-authors
- Otmar Scherzer (20 shared papers)Markus Haltmeier (12 shared papers)Frank Lenzen (11 shared papers)Harald Grossauer (8 shared papers)Günther Ernst (1 shared paper)Herbert Thiele (1 shared paper)Theodore Alexandrov (1 shared paper)Sören‐Oliver Deininger (1 shared paper)
In The Last Decade
Markus Grasmair
36 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 89
- Mathematical Physics 512
- Computational Mechanics 508
- Computer Vision and Pattern Recognition 307
- Applied Mathematics 93
- Spectroscopy 122
Countries citing papers authored by Markus Grasmair
This map shows the geographic impact of Markus Grasmair'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 Markus Grasmair with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Grasmair more than expected).
Fields of papers citing papers by Markus Grasmair
This network shows the impact of papers produced by Markus Grasmair. 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 Markus Grasmair. The network helps show where Markus Grasmair may publish in the future.
Co-authors
The 24 scholars most cited alongside Markus Grasmair, 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 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 320 | |
| 2 | 2010 | 163 | |
| 3 | 2008 | 124 | |
| 4 | 2010 | 85 | |
| 5 | 2010 | 67 | |
| 6 | 2010 | 60 | |
| 7 | 2009 | 46 | |
| 8 | 2009 | 30 | |
| 9 | 2009 | 28 | |
| 10 | 2011 | 26 | |
| 11 | 2011 | 25 | |
| 12 | 2006 | 24 | |
| 13 | 2013 | 14 | |
| 14 | 2008 | 13 | |
| 15 | 2022 | 9 | |
| 16 | 2014 | 7 | |
| 17 | 2008 | 7 | |
| 18 | 2021 | 6 | |
| 19 | 2005 | 6 | |
| 20 | 2012 | 5 |
About Markus Grasmair
Markus Grasmair is a scholar working on Mathematical Physics, Computational Mechanics, Computer Vision and Pattern Recognition, Applied Mathematics and Computational Theory and Mathematics, having authored 41 papers that have together received 1.1k indexed citations. Recurring topics across this work include Numerical methods in inverse problems (19 papers), Sparse and Compressive Sensing Techniques (15 papers), Image and Signal Denoising Methods (7 papers), Medical Image Segmentation Techniques (5 papers), Image and Object Detection Techniques (3 papers), Photoacoustic and Ultrasonic Imaging (3 papers), Advanced Mathematical Modeling in Engineering (3 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Mathematical Physics (512 citations), Computational Mechanics (508 citations), Computer Vision and Pattern Recognition (307 citations), Applied Mathematics (93 citations) and Spectroscopy (122 citations). Markus Grasmair has collaborated with scholars based in Austria, Norway and Germany. Frequent co-authors include Otmar Scherzer, Markus Haltmeier, Frank Lenzen, Harald Grossauer, Günther Ernst, Herbert Thiele, Theodore Alexandrov, Sören‐Oliver Deininger, Peter Maaß and Michael Becker. Their work appears in journals such as Inverse Problems, Numerical Functional Analysis and Optimization, Inverse Problems and Imaging, SIAM Journal on Imaging Sciences and Journal of Mathematical Imaging and Vision.
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.