M. Lungaroni
Impact in
- Nuclear and High Energy Physics top 10%
- Magnetic confinement fusion research
- Signal Processing top 10%
- Time Series Analysis and Forecasting
Papers in
-
- Magnetic confinement fusion research 17
-
- Nuclear reactor physics and engineering 9
- Co-authors
- A. Murari (35 shared papers)M. Gelfusa (34 shared papers)E. Peluso (27 shared papers)P. Gaudio (27 shared papers)J. Vega (13 shared papers)Riccardo Rossi (10 shared papers)M. Baruzzo (2 shared papers)L. Garzotti (3 shared papers)
- Journals
- Nuclear Fusion (11 papers)Fusion Engineering and Design (5 papers)Journal of Instrumentation (3 papers)Scientific Reports (2 papers)Plasma Physics and Controlled Fusion (2 papers)
- Partner nations
- ItalySpainUnited Kingdom
In The Last Decade
M. Lungaroni
36 papers receiving 340 citations
Peers
Comparison fields: 5 of 78
- Nuclear and High Energy Physics 145
- Signal Processing 50
- Artificial Intelligence 120
- Radiation 32
- Statistical and Nonlinear Physics 43
Countries citing papers authored by M. Lungaroni
This map shows the geographic impact of M. Lungaroni'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. Lungaroni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Lungaroni more than expected).
Fields of papers citing papers by M. Lungaroni
This network shows the impact of papers produced by M. Lungaroni. 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. Lungaroni. The network helps show where M. Lungaroni may publish in the future.
Co-authors
The 25 scholars most cited alongside M. Lungaroni, 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 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 46 | |
| 2 | 2020 | 30 | |
| 3 | 2015 | 30 | |
| 4 | 2019 | 28 | |
| 5 | 2020 | 24 | |
| 6 | 2014 | 23 | |
| 7 | 2015 | 21 | |
| 8 | 2020 | 19 | |
| 9 | 2019 | 18 | |
| 10 | 2018 | 14 | |
| 11 | 2020 | 12 | |
| 12 | 2020 | 11 | |
| 13 | 2016 | 11 | |
| 14 | 2018 | 10 | |
| 15 | 2024 | 9 | |
| 16 | 2019 | 8 | |
| 17 | 2015 | 8 | |
| 18 | 2017 | 6 | |
| 19 | 2021 | 5 | |
| 20 | 2020 | 5 |
About M. Lungaroni
M. Lungaroni is a scholar working on Nuclear and High Energy Physics, Aerospace Engineering, Artificial Intelligence, Signal Processing and Materials Chemistry, having authored 42 papers that have together received 371 indexed citations. Recurring topics across this work include Magnetic confinement fusion research (17 papers), Nuclear reactor physics and engineering (9 papers), Time Series Analysis and Forecasting (7 papers), Fusion materials and technologies (5 papers), Neural Networks and Applications (4 papers), Spectroscopy and Chemometric Analyses (4 papers), Advanced Statistical Methods and Models (4 papers) and Advanced Optical Sensing Technologies (4 papers). The work is most often cited by research in Nuclear and High Energy Physics (145 citations), Signal Processing (50 citations), Artificial Intelligence (120 citations), Radiation (32 citations) and Statistical and Nonlinear Physics (43 citations). M. Lungaroni has collaborated with scholars based in Italy, Spain and United Kingdom. Frequent co-authors include A. Murari, M. Gelfusa, E. Peluso, P. Gaudio, J. Vega, Riccardo Rossi, M. Baruzzo, L. Garzotti, S. Dormido-Canto and Francesco Cianfrani. Their work appears in journals such as Nuclear Fusion, Fusion Engineering and Design, Journal of Instrumentation, Scientific Reports and Plasma Physics and Controlled Fusion.
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.