C. Galperti

114 papers receiving 2.5k citations

C. Galperti's Hit Papers

Magnetic control of tokamak plasmas through deep reinforcement learning 2022 · 518 citations
5180+1+2Years since publication100200300400500

Peers

C. Galperti
Comparison fields: 5 of 90
  • Nuclear and High Energy Physics 1.5k
  • Computer Networks and Communications 336
  • Statistical and Nonlinear Physics 120
  • Control and Systems Engineering 154
  • Artificial Intelligence 197
Replace José Sánchez with:
José Sánchez Spain
Jun Su China
S. Dormido-Canto Spain
Massimiliano Mattei Italy
G. Manduchi Italy
Zhipeng Chen China
David C. Reid Switzerland
Shinichi Nakasuka Japan
Ling Qiang Yang China
Jun Fan United States
C. Galperti relative to José Sánchez Spain José Sánchez's profile →
Citations per field
00.5×2×4×5.4×
José Sánchez · 1×
Citations per year

Countries citing papers authored by C. Galperti

Since Specialization
Citations

This map shows the geographic impact of C. Galperti'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 C. Galperti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Galperti more than expected).

Fields of papers citing papers by C. Galperti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Galperti. 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 C. Galperti. The network helps show where C. Galperti may publish in the future.

Co-authors

The 25 scholars most cited alongside C. Galperti, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with C. Galperti Line = papers co-authored together C. Galperti links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 117 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Magnetic control of tokamak plasmas through deep reinforcement learning
Hit paper breakdown →
2022518
2 2008342
3 2010225
4 201876
5 200774
6 201974
7 202164
8 201759
9 202259
10 201953
11 201751
12 201948
13 201737
14 201734
15 201831
16 201928
17 202028
18 202428
19 201027
20 201726

About C. Galperti

C. Galperti is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications, Statistical and Nonlinear Physics, Control and Systems Engineering and Energy Engineering and Power Technology, having authored 117 papers that have together received 2.7k indexed citations. Recurring topics across this work include Magnetic confinement fusion research (102 papers), Energy Efficient Wireless Sensor Networks (7 papers), Fusion and Plasma Physics Studies (5 papers), Wireless Sensor Networks for Data Analysis (5 papers), Advanced Control Systems Optimization (3 papers), Particle accelerators and beam dynamics (3 papers), Power Systems and Renewable Energy (2 papers) and Landslides and related hazards (2 papers). The work is most often cited by research in Nuclear and High Energy Physics (1.5k citations), Computer Networks and Communications (336 citations), Statistical and Nonlinear Physics (120 citations), Control and Systems Engineering (154 citations) and Artificial Intelligence (197 citations). C. Galperti has collaborated with scholars based in Italy, Switzerland and Germany. Frequent co-authors include Cesare Alippi, Manuel Roveri, Romolo Camplani, Federico Felici, Olivier Sauter, Basil Duval, S. Coda, J.M. Moret, Giuseppe Anastasi and Francesca Romana Mancini. Their work appears in journals such as Nuclear Fusion, Fusion Engineering and Design, Plasma Physics and Controlled Fusion, Journal of Instrumentation and Review of Scientific Instruments.

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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