M. Marinaro

1.2k total citations
98 papers, 794 citations indexed

About

M. Marinaro is a scholar working on Condensed Matter Physics, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, M. Marinaro has authored 98 papers receiving a total of 794 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Condensed Matter Physics, 27 papers in Atomic and Molecular Physics, and Optics and 16 papers in Artificial Intelligence. Recurrent topics in M. Marinaro's work include Physics of Superconductivity and Magnetism (22 papers), Theoretical and Computational Physics (14 papers) and Quantum and electron transport phenomena (13 papers). M. Marinaro is often cited by papers focused on Physics of Superconductivity and Magnetism (22 papers), Theoretical and Computational Physics (14 papers) and Quantum and electron transport phenomena (13 papers). M. Marinaro collaborates with scholars based in Italy, Moldova and United Kingdom. M. Marinaro's co-authors include Silvia Scarpetta, Anna Esposito, F. Mancini, Flora Giudicepietro, M. Martini, Francesco Guerra, E. R. Caianiello, H. Matsumoto, Edoardo Del Pezzo and Simona Petrosino and has published in prestigious journals such as Physical review. B, Condensed matter, Physical Review B and Physics Reports.

In The Last Decade

M. Marinaro

88 papers receiving 734 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Marinaro Italy 13 292 183 172 156 104 98 794
G. Györgyi Hungary 22 170 0.6× 40 0.2× 247 1.4× 301 1.9× 442 4.3× 57 1.2k
Ariel Caticha United States 15 156 0.5× 32 0.2× 210 1.2× 263 1.7× 296 2.8× 91 885
Bernard Nienhuis Netherlands 20 212 0.7× 25 0.1× 448 2.6× 978 6.3× 275 2.6× 47 1.7k
R. L. Stratonovich Russia 10 262 0.9× 16 0.1× 248 1.4× 103 0.7× 415 4.0× 27 1.1k
C. Ray Smith United States 6 99 0.3× 27 0.1× 64 0.4× 26 0.2× 124 1.2× 7 544
M. A. H. Nerenberg Canada 14 81 0.3× 61 0.3× 140 0.8× 114 0.7× 314 3.0× 46 853
Mikito Toda Japan 15 144 0.5× 29 0.2× 860 5.0× 131 0.8× 920 8.8× 50 1.7k
G. Grasseau France 8 52 0.2× 39 0.2× 40 0.2× 292 1.9× 159 1.5× 10 800
Yurii A. Kravtsov Russia 10 35 0.1× 146 0.8× 580 3.4× 24 0.2× 263 2.5× 10 1.3k
Andrew G. Klein United States 20 53 0.2× 41 0.2× 564 3.3× 32 0.2× 79 0.8× 96 1.3k

Countries citing papers authored by M. Marinaro

Since Specialization
Citations

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

Fields of papers citing papers by M. Marinaro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Marinaro

This figure shows the co-authorship network connecting the top 25 collaborators of M. Marinaro. A scholar is included among the top collaborators of M. Marinaro 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. Marinaro. M. Marinaro 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.
Apolloni, Bruno, Simone Bassis, & M. Marinaro. (2009). New Directions in Neural Networks: 18th Italian Workshop on Neural Networks WIRN 2008 - Volume 193 Frontiers in Artificial Intelligence and Applications. IOS Press eBooks. 3 indexed citations
2.
Esposito, Anna, Amir Hussain, M. Marinaro, & R. Martone. (2009). Multimodal Signals: Cognitive and Algorithmic Issues: COST Action 2102 and euCognition International School Vietri sul Mare, Italy, April 21-26, 2008 Revised Selected and Invited Papers. Medical Entomology and Zoology. 4 indexed citations
3.
Apolloni, Bruno, M. Marinaro, Giuseppe Nicosia, & Roberto Tagliaferri. (2006). Neural Nets: 16th Italian Workshop on Neural Nets, WIRN 2005, International Workshop on Natural and Artificial Immune Systems, NAIS 2005, Vietri sul Mare, ... Papers (Lecture Notes in Computer Science). Springer eBooks.
4.
Marinaro, M., et al.. (2006). Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule. Mathematical Biosciences. 207(2). 322–335. 3 indexed citations
5.
Chollet, Gérard, Anna Esposito, Marcos Faúndez-Zanuy, & M. Marinaro. (2005). Nonlinear Speech Modeling and Applications: advanced Lectures and Revised Selected Papers. DIAL (Catholic University of Leuven). 4 indexed citations
6.
Chollet, Gérard, Anna Esposito, Marcos Faúndez-Zanuy, & M. Marinaro. (2005). Nonlinear Speech Modeling and Applications: Advanced Lectures and Revised Selected Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer eBooks. 5 indexed citations
7.
Citro, R. & M. Marinaro. (2001). Charge stripes in the extended Hubbard model with nearest-neighbor Coulomb interaction. The European Physical Journal B. 22(3). 343–349. 11 indexed citations
8.
Marinaro, M. & Silvia Scarpetta. (2000). On-line learning in RBF neural networks: a stochastic approach. Neural Networks. 13(7). 719–729. 14 indexed citations
9.
Esposito, Anna, et al.. (2000). Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm. Neural Networks. 13(6). 651–665. 49 indexed citations
10.
Esposito, Anna, M. Marinaro, & Silvia Scarpetta. (1998). An incremental local radial basis function network.. The European Symposium on Artificial Neural Networks. 21–26.
11.
Citro, R. & M. Marinaro. (1998). Doping and temperature dependence of the specific heat in the p-d model. Solid State Communications. 106(11). 745–749.
12.
Marinaro, M., et al.. (1994). Icann 94: Proceedings of the International Conference on Artificial Neural Networks, Sorrento, Italy,26-29 May 1994. Medical Entomology and Zoology. 2 indexed citations
13.
Marinaro, M., et al.. (1992). Structure : from physics to general sytems : Festschrift volume in honour of E.R. Caianiello on his seventieth birthday : Amalfi, Salerno, Italy, 20-24 October 1991. WORLD SCIENTIFIC eBooks. 1 indexed citations
14.
Caianiello, E. R., et al.. (1992). Can Spurious States Be Useful. Complex Systems. 6. 1 indexed citations
15.
Coniglio, Antonio & M. Marinaro. (1973). Weak and strong scaling as a generalization of Kadanoff's picture. Physica. 66(2). 385–394. 1 indexed citations
16.
Marinaro, M.. (1972). Comparison of different approaches to renormalization. Nuovo cimento della Società italiana di fisica. A, Nuclei, particles and fields. 9(1). 62–74. 1 indexed citations
17.
Caianiello, E. R., M. Marinaro, & G. Scarpetta. (1971). Self-consistent derivation of multiple mass values in renormalized field theories. Nuovo cimento della Società italiana di fisica. A, Nuclei, particles and fields. 3(1). 195–219. 10 indexed citations
18.
Caianiello, E. R. & M. Marinaro. (1971). Mass equation from tadpoles: Nonpolynomial Lagrangians. Lettere al nuovo cimento della societa italiana di fisica/Lettere al nuovo cimento. 1(22). 899–902. 1 indexed citations
19.
Marinaro, M. & Katsumi Tanaka. (1962). The pion-pion effect on low-energy pion-nucleon scattering. Il Nuovo Cimento. 23(3). 537–546. 2 indexed citations
20.
Marinaro, M., et al.. (1960). Note on possible rare decay modes for elementary particles. Il Nuovo Cimento. 15(6). 934–936. 7 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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