Tomas Sauer

66 papers receiving 497 citations

Peers

Tomas Sauer
Comparison fields: 5 of 77
  • Computational Mechanics 244
  • Computer Vision and Pattern Recognition 126
  • Computer Graphics and Computer-Aided Design 20
  • Computational Theory and Mathematics 88
  • Applied Mathematics 47
Replace Xiao-Diao Chen with:
Xiao-Diao Chen China
Xiquan Shi China
K. Unsworth New Zealand
Hwan Pyo Moon South Korea
Lucia Romani Italy
Chongyang Deng China
Frédéric de Gournay France
Yasuyuki Sugaya Japan
Fengqun Zhao China
Knut Mørken Norway
Tomas Sauer relative to Xiao-Diao Chen China Xiao-Diao Chen's profile →
Citations per field
00.5×1.5×1.9×
Xiao-Diao Chen · 1×
Citations per year

Countries citing papers authored by Tomas Sauer

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Tomas Sauer Line = papers co-authored together Tomas Sauer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202053
2 200935
3 201128
4 201621
5 200620
6
Stationary vector subdivision-quotient ideals, differences and approximation power
200219
7 201819
8 200418
9 201117
10 201914
11 201614
12 200714
13 201713
14 201812
15 200312
16 201112
17 201011
18 200210
19 200910
20 20079

About Tomas Sauer

Tomas Sauer is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Signal Processing and Mechanical Engineering, having authored 72 papers that have together received 516 indexed citations. Recurring topics across this work include Advanced Numerical Analysis Techniques (30 papers), Image and Signal Denoising Methods (16 papers), Polynomial and algebraic computation (16 papers), Digital Filter Design and Implementation (9 papers), Tribology and Lubrication Engineering (6 papers), Advanced machining processes and optimization (5 papers), Image and Object Detection Techniques (4 papers) and Commutative Algebra and Its Applications (4 papers). The work is most often cited by research in Computational Mechanics (244 citations), Computer Vision and Pattern Recognition (126 citations), Computer Graphics and Computer-Aided Design (20 citations), Computational Theory and Mathematics (88 citations) and Applied Mathematics (47 citations). Tomas Sauer has collaborated with scholars based in Germany, Italy and Spain. Frequent co-authors include Mariantonia Cotronei, Jean‐Louis Merrien, Gitta Kutyniok, Costanza Conti, Achim Langenbucher, Filippo Giammaria Praticò, Rosario Fedele, Vitalii Naumov, 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.

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