M. Defurne
Impact in
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- Quantum Chromodynamics and Particle Interactions
- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Black Holes and Theoretical Physics
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- Stochastic processes and statistical mechanics
Papers in
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- Quantum Chromodynamics and Particle Interactions 5
- High-Energy Particle Collisions Research 5
- Particle physics theoretical and experimental studies 5
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- Evolutionary Algorithms and Applications 1
- Reinforcement Learning in Robotics 1
- Metaheuristic Optimization Algorithms Research 1
- Co-authors
- Valerio Bertone (4 shared papers)H. Moutarde (4 shared papers)J. Rodrı́guez-Quintero (4 shared papers)F. De Soto (4 shared papers)Jorge Segovia (4 shared papers)Cédric Mezrag (4 shared papers)F. Sabatié (1 shared paper)O. Bessidskaia Bylund (1 shared paper)
- Journals
- Physical review. D (2 papers)Few-Body Systems (1 paper)Physical Review Letters (1 paper)Repositorio Institucional de la Universidad de Huelva (1 paper)arXiv (Cornell University) (1 paper)
In The Last Decade
M. Defurne
6 papers receiving 73 citations
Peers
Comparison fields: 5 of 10
- Nuclear and High Energy Physics 66
- Mathematical Physics 2
- Condensed Matter Physics 2
- Artificial Intelligence 5
- Information Systems and Management 1
Countries citing papers authored by M. Defurne
This map shows the geographic impact of M. Defurne'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 M. Defurne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Defurne more than expected).
Fields of papers citing papers by M. Defurne
This network shows the impact of papers produced by M. Defurne. 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 M. Defurne. The network helps show where M. Defurne may publish in the future.
Co-authors
The 9 scholars most cited alongside M. Defurne, 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 | 2022 | 34 | |
| 2 | 2022 | 30 | |
| 3 | 2019 | 5 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 1 |
About M. Defurne
M. Defurne is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 6 papers that have together received 74 indexed citations. Recurring topics across this work include Quantum Chromodynamics and Particle Interactions (5 papers), High-Energy Particle Collisions Research (5 papers), Particle physics theoretical and experimental studies (5 papers), Evolutionary Algorithms and Applications (1 paper), Reinforcement Learning in Robotics (1 paper) and Metaheuristic Optimization Algorithms Research (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (66 citations), Mathematical Physics (2 citations), Condensed Matter Physics (2 citations), Artificial Intelligence (5 citations) and Information Systems and Management (1 citation). M. Defurne has collaborated with scholars based in France and Spain. Frequent co-authors include Valerio Bertone, H. Moutarde, J. Rodrı́guez-Quintero, F. De Soto, Jorge Segovia, Cédric Mezrag, F. Sabatié, O. Bessidskaia Bylund and P.A.M. Guichon. Their work appears in journals such as Physical review. D, Few-Body Systems, Physical Review Letters, Repositorio Institucional de la Universidad de Huelva and arXiv (Cornell University).
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.