Francesco Marchetti
- Computational Mechanics top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Mechanics of Materials
- Astronomy and Astrophysics
- Co-authors
- Stefano De MarchıEmma PerracchioneMichele PianaCristina CampiDavide PoggialiLeevan LingWolfgang ErbFederico Benvenuto
- Topics
- Advanced Numerical Analysis Techniques (7 papers)Numerical methods in engineering (6 papers)Image and Signal Denoising Methods (4 papers)
- Journals
- The Astrophysical JournalIEEE Transactions on Pattern Analysis and Machine IntelligencePattern Recognition
- Partner nations
- ItalyUnited StatesSwitzerland
In The Last Decade
Francesco Marchetti
33 papers receiving 285 citations
Peers
Comparison fields: 5 of 71
- Computational Mechanics 76
- Artificial Intelligence 70
- Computer Vision and Pattern Recognition 64
- Mechanics of Materials 60
- Astronomy and Astrophysics 47
Countries citing papers authored by Francesco Marchetti
This map shows the geographic impact of Francesco Marchetti'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 Francesco Marchetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Marchetti more than expected).
Fields of papers citing papers by Francesco Marchetti
This network shows the impact of papers produced by Francesco Marchetti. 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 Francesco Marchetti. The network helps show where Francesco Marchetti may publish in the future.
Co-authorship network of co-authors of Francesco Marchetti
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Marchetti. A scholar is included among the top collaborators of Francesco Marchetti 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 Francesco Marchetti. Francesco Marchetti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 5 | |
| 5 | 11 | |
| 6 | 13 | |
| 7 | 2 | |
| 8 | 15 | |
| 9 | 23 | |
| 10 | 1 | |
| 11 | Convergence rate in terms of the continuous SSIM (cSSIM) index in RBF interpolation | 2 |
| 12 | 6 | |
| 13 | 7 | |
| 14 | 14 | |
| 15 | 1 | |
| 16 | 13 | |
| 17 | 26 | |
| 18 | 19 | |
| 19 | 11 | |
| 20 | 6 |
About Francesco Marchetti
Francesco Marchetti is a scholar working on Numerical Analysis, Modeling and Simulation and Computer Vision and Pattern Recognition, having authored 36 papers that have together received 316 indexed citations. Recurring topics across this work include Advanced Numerical Analysis Techniques (7 papers), Numerical methods in engineering (6 papers) and Image and Signal Denoising Methods (4 papers). The work is most often cited by research in Numerical Analysis (34 citations), Modeling and Simulation (24 citations) and Computational Mechanics (76 citations). Francesco Marchetti has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Stefano De Marchı, Emma Perracchione, Michele Piana, Cristina Campi, Davide Poggiali, Leevan Ling, Wolfgang Erb, Federico Benvenuto, Federico Becattini and Lorenzo Seidenari. Their work appears in journals such as The Astrophysical Journal, IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition.
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.