Jan Vybíral
- Applied Mathematics top 2%
- Advanced Harmonic Analysis Research 20
- Mathematical Analysis and Transform Methods 14
- Nonlinear Partial Differential Equations 5
- Numerical Analysis top 5%
- Mathematical Approximation and Integration 17
- Mathematical Physics top 5%
- Advanced Mathematical Physics Problems 5
- Materials Chemistry top 10%
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- Sparse and Compressive Sensing Techniques 5
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- Computational Geometry and Mesh Generation 3
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- Machine Learning and Algorithms 3
- Co-authors
- Luca M. GhiringhelliClaudia DraxlSergey V. LevchenkoMatthias SchefflerHenning KempkaAicke HinrichsKarin SchnassGitta Kutyniok
- Journals
- Physical Review Letters (1 paper)SHILAP Revista de lepidopterología (1 paper)Journal of Functional Analysis (1 paper)
In The Last Decade
Jan Vybíral
38 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 81
- Applied Mathematics 344
- Numerical Analysis 168
- Mathematical Physics 170
- Materials Chemistry 582
- Computational Theory and Mathematics 198
Countries citing papers authored by Jan Vybíral
This map shows the geographic impact of Jan Vybíral'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 Jan Vybíral with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Vybíral more than expected).
Fields of papers citing papers by Jan Vybíral
This network shows the impact of papers produced by Jan Vybíral. 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 Jan Vybíral. The network helps show where Jan Vybíral may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Jan Vybíral, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 0 | |
| 3 | 2022 | 6 | |
| 4 | On the volume of unit balls of finite-dimensional Lorentz spaces | 2019 | 3 |
| 5 | 2019 | 15 | |
| 6 | 2018 | 2 | |
| 7 | 2015 | 2 | |
| 8 | Big Data of Materials Science: Critical Role of the Descriptorbreakdown → | 2015 | 680 |
| 9 | 2014 | 15 | |
| 10 | 2013 | 51 | |
| 11 | 2013 | 11 | |
| 12 | 2013 | 22 | |
| 13 | 2012 | 47 | |
| 14 | 2011 | 5 | |
| 15 | 2009 | 9 | |
| 16 | 2008 | 29 | |
| 17 | 2008 | 22 | |
| 18 | 2007 | 14 | |
| 19 | 2007 | 1 | |
| 20 | 2005 | 1 |
About Jan Vybíral
Jan Vybíral is a scholar working on Numerical Analysis, Applied Mathematics and Mathematical Physics, having authored 40 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Harmonic Analysis Research (20 papers), Mathematical Approximation and Integration (17 papers), Mathematical Analysis and Transform Methods (14 papers), Sparse and Compressive Sensing Techniques (5 papers), Nonlinear Partial Differential Equations (5 papers), Advanced Mathematical Physics Problems (5 papers), Computational Geometry and Mesh Generation (3 papers) and Machine Learning and Algorithms (3 papers). The work is most often cited by research in Applied Mathematics (344 citations), Numerical Analysis (168 citations) and Mathematical Physics (170 citations). Jan Vybíral has collaborated with scholars based in Germany, Czechia and Austria. Frequent co-authors include Luca M. Ghiringhelli, Claudia Draxl, Sergey V. Levchenko, Matthias Scheffler, Henning Kempka, Aicke Hinrichs, Karin Schnass, Gitta Kutyniok, Massimo Fornasier and Robert Calderbank. Their work appears in journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Journal of Functional Analysis.
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.