M. Lungaroni

1.2k total citations
42 papers, 371 citations indexed

About

M. Lungaroni is a scholar working on Nuclear and High Energy Physics, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, M. Lungaroni has authored 42 papers receiving a total of 371 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Nuclear and High Energy Physics, 13 papers in Aerospace Engineering and 12 papers in Artificial Intelligence. Recurrent topics in M. Lungaroni's work include Magnetic confinement fusion research (17 papers), Nuclear reactor physics and engineering (9 papers) and Time Series Analysis and Forecasting (7 papers). M. Lungaroni is often cited by papers focused on Magnetic confinement fusion research (17 papers), Nuclear reactor physics and engineering (9 papers) and Time Series Analysis and Forecasting (7 papers). M. Lungaroni collaborates with scholars based in Italy, Spain and United Kingdom. M. Lungaroni's co-authors include A. Murari, M. Gelfusa, E. Peluso, P. Gaudio, J. Vega, Riccardo Rossi, M. Baruzzo, L. Garzotti, S. Dormido-Canto and D. Frigione and has published in prestigious journals such as Scientific Reports, Applied Sciences and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

M. Lungaroni

36 papers receiving 340 citations

Peers

M. Lungaroni
E. Peluso Italy
A. Pereira Spain
Alexey Svyatkovskiy United States
Kevin Montes United States
Keith Erickson United States
Malachi Schram United States
C.H. Vincent United Kingdom
G. Ososkov Russia
J. Seixas Switzerland
R. Frühwirth Austria
E. Peluso Italy
M. Lungaroni
Citations per year, relative to M. Lungaroni M. Lungaroni (= 1×) peers E. Peluso

Countries citing papers authored by M. Lungaroni

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of M. Lungaroni

This figure shows the co-authorship network connecting the top 25 collaborators of M. Lungaroni. A scholar is included among the top collaborators of M. Lungaroni based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Lungaroni. M. Lungaroni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lungaroni, M., S. Noce, N. Fonnesu, et al.. (2025). Neutronics studies on the European DEMO divertor target supports. Fusion Engineering and Design. 222. 115524–115524.
2.
Colangeli, A., D. Flammini, N. Fonnesu, et al.. (2025). Preliminary study for a machine learning model for GENeuSIS. Fusion Engineering and Design. 224. 115594–115594.
3.
4.
Fonnesu, N., P. Beaumont, A. Colangeli, et al.. (2025). ITER-relevant experimental neutronic activities at JET during DTE3 and at the Frascati neutron generator. Fusion Engineering and Design. 219. 115297–115297.
5.
Federici, G., M. Siccinio, C. Bachmann, et al.. (2024). Reply to Comment on ‘Relationship between magnetic field and tokamak size—a system engineering perspective and implications to fusion development’. Nuclear Fusion. 64(10). 108002–108002. 1 indexed citations
6.
Federici, G., et al.. (2024). Relationship between magnetic field and tokamak size—a system engineering perspective and implications to fusion development. Nuclear Fusion. 64(3). 36025–36025. 9 indexed citations
7.
8.
Murari, A., E. Peluso, T. Craciunescu, et al.. (2021). Frontiers in data analysis methods: from causality detection to data driven experimental design. Plasma Physics and Controlled Fusion. 64(2). 24002–24002. 1 indexed citations
9.
Murari, A., M. Gelfusa, M. Lungaroni, P. Gaudio, & E. Peluso. (2021). A systemic approach to classification for knowledge discovery with applications to the identification of boundary equations in complex systems. Artificial Intelligence Review. 55(1). 255–289. 5 indexed citations
10.
Murari, A., Riccardo Rossi, E. Peluso, et al.. (2020). On the transfer of adaptive predictors between different devices for both mitigation and prevention of disruptions. Nuclear Fusion. 60(5). 56003–56003. 30 indexed citations
11.
Murari, A., Riccardo Rossi, M. Lungaroni, M. Baruzzo, & M. Gelfusa. (2020). Stacking of predictors for the automatic classification of disruption types to optimize the control logic. Nuclear Fusion. 61(3). 36027–36027. 19 indexed citations
12.
Murari, A., et al.. (2020). Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools. Applied Sciences. 10(19). 6683–6683. 12 indexed citations
13.
Murari, A., Riccardo Rossi, M. Lungaroni, P. Gaudio, & M. Gelfusa. (2020). Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques. Entropy. 22(2). 141–141. 5 indexed citations
14.
Murari, A., E. Peluso, M. Lungaroni, et al.. (2020). Data driven theory for knowledge discovery in the exact sciences with applications to thermonuclear fusion. Scientific Reports. 10(1). 19858–19858. 11 indexed citations
15.
Murari, A., et al.. (2019). Adaptive learning for disruption prediction in non-stationary conditions. Nuclear Fusion. 59(8). 86037–86037. 28 indexed citations
16.
Murari, A., M. Lungaroni, & M. Gelfusa. (2019). Testing the consistency of multimachine databases for physical studies of regression. Nuclear Fusion. 60(1). 15001–15001. 1 indexed citations
17.
Murari, A., M. Lungaroni, E. Peluso, T. Craciunescu, & M. Gelfusa. (2019). A Model Falsification Approach to Learning in Non-Stationary Environments for Experimental Design. Scientific Reports. 9(1). 17880–17880. 8 indexed citations
18.
Lungaroni, M., A. Murari, E. Peluso, P. Gaudio, & M. Gelfusa. (2019). Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems. Complexity. 2019(1). 2 indexed citations
19.
Murari, A., E. Peluso, Francesco Cianfrani, P. Gaudio, & M. Lungaroni. (2019). On the Use of Entropy to Improve Model Selection Criteria. Entropy. 21(4). 394–394. 18 indexed citations
20.
Murari, A., M. Lungaroni, E. Peluso, et al.. (2018). On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals. Entropy. 20(9). 627–627. 14 indexed citations

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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