Lev Vidmar
- Computational Mathematics top 2%
- Statistical and Nonlinear Physics top 0.5%
- Opinion Dynamics and Social Influence 18
- Model Reduction and Neural Networks 9
- Quantum chaos and dynamical systems 9
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- Quantum many-body systems 55
- Cold Atom Physics and Bose-Einstein Condensates 29
- Quantum and electron transport phenomena 16
- Spectroscopy and Quantum Chemical Studies 4
- Condensed Matter Physics top 2%
- Physics of Superconductivity and Magnetism 17
- Artificial Intelligence top 5%
- Co-authors
- Marcos RigolJ. BončaJan ŠuntajsTomaž ProsenFabian Heidrich‐MeisnerMarcin MierzejewskiEugenio BianchiLucas Hackl
- Cited by
- Computational MathematicsStatistical and Nonlinear PhysicsAtomic and Molecular Physics, and Optics
- Journals
- Physical Review Letters (17 papers)Physical review. B. (15 papers)Physical Review B (10 papers)
- Partner nations
- SloveniaUnited StatesPoland
In The Last Decade
Lev Vidmar
64 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 40
- Computational Mathematics 59
- Statistical and Nonlinear Physics 995
- Atomic and Molecular Physics, and Optics 2.2k
- Condensed Matter Physics 730
- Artificial Intelligence 314
Countries citing papers authored by Lev Vidmar
This map shows the geographic impact of Lev Vidmar'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 Lev Vidmar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lev Vidmar more than expected).
Fields of papers citing papers by Lev Vidmar
This network shows the impact of papers produced by Lev Vidmar. 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 Lev Vidmar. The network helps show where Lev Vidmar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Lev Vidmar, 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 | 2025 | 1 | |
| 2 | 2024 | 48 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 5 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 13 | |
| 9 | 2023 | 10 | |
| 10 | 2022 | 13 | |
| 11 | 2021 | 29 | |
| 12 | Quantum chaos challenges many-body localizationbreakdown → | 2020 | 241 |
| 13 | 2020 | 43 | |
| 14 | 2019 | 75 | |
| 15 | 2017 | 7 | |
| 16 | 2017 | 112 | |
| 17 | 2012 | 55 | |
| 18 | 2012 | 9 | |
| 19 | 2011 | 33 | |
| 20 | 2009 | 24 |
About Lev Vidmar
Lev Vidmar is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics, Condensed Matter Physics and Electronic, Optical and Magnetic Materials, having authored 66 papers that have together received 2.3k indexed citations. Recurring topics across this work include Quantum many-body systems (55 papers), Cold Atom Physics and Bose-Einstein Condensates (29 papers), Opinion Dynamics and Social Influence (18 papers), Physics of Superconductivity and Magnetism (17 papers), Quantum and electron transport phenomena (16 papers), Model Reduction and Neural Networks (9 papers), Quantum chaos and dynamical systems (9 papers) and Spectroscopy and Quantum Chemical Studies (4 papers). The work is most often cited by research in Computational Mathematics (59 citations), Statistical and Nonlinear Physics (995 citations), Atomic and Molecular Physics, and Optics (2.2k citations), Condensed Matter Physics (730 citations) and Artificial Intelligence (314 citations). Lev Vidmar has collaborated with scholars based in Slovenia, United States and Poland. Frequent co-authors include Marcos Rigol, J. Bonča, Jan Šuntajs, Tomaž Prosen, Fabian Heidrich‐Meisner, Marcin Mierzejewski, Eugenio Bianchi, Lucas Hackl, Denis Golež and S. A. Trugman. Their work appears in journals such as Physical Review Letters, Physical review. B., Physical Review B, Physical review. E and Physical review. A.
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.