Francesco Rizzi
- Statistics, Probability and Uncertainty top 2%
- Materials Chemistry
- Statistical and Nonlinear Physics top 10%
- Computational Mechanics
- Molecular Biology
- Co-authors
- Bert DebusschereOmar KnioMaher SalloumReese E. JonesKhachik SargsyanHabib N. NajmOmar M. KnioHelgi Adalsteinsson
- Topics
- Probabilistic and Robust Engineering Design (18 papers)Model Reduction and Neural Networks (14 papers)Parallel Computing and Optimization Techniques (8 papers)
- Cited by
- Statistics, Probability and UncertaintyStatistical and Nonlinear PhysicsFluid Flow and Transfer Processes
- Journals
- The Journal of Chemical PhysicsComputer Methods in Applied Mechanics and EngineeringAIAA Journal
- Partner nations
- United StatesFranceSaudi Arabia
In The Last Decade
Francesco Rizzi
25 papers receiving 346 citations
Peers
Comparison fields: 5 of 61
- Statistics, Probability and Uncertainty 157
- Materials Chemistry 119
- Statistical and Nonlinear Physics 87
- Computational Mechanics 54
- Molecular Biology 45
Countries citing papers authored by Francesco Rizzi
This map shows the geographic impact of Francesco Rizzi'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 Rizzi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Rizzi more than expected).
Fields of papers citing papers by Francesco Rizzi
This network shows the impact of papers produced by Francesco Rizzi. 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 Rizzi. The network helps show where Francesco Rizzi may publish in the future.
Co-authorship network of co-authors of Francesco Rizzi
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Rizzi. A scholar is included among the top collaborators of Francesco Rizzi 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 Rizzi. Francesco Rizzi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 9 | |
| 8 | 10 | |
| 9 | 10 | |
| 10 | 8 | |
| 11 | 6 | |
| 12 | 1 | |
| 13 | 0 | |
| 14 | 28 | |
| 15 | 2 | |
| 16 | 9 | |
| 17 | 33 | |
| 18 | 33 | |
| 19 | 58 | |
| 20 | 50 |
About Francesco Rizzi
Francesco Rizzi is a scholar working on Statistics, Probability and Uncertainty, Computational Mathematics and Hardware and Architecture, having authored 31 papers that have together received 351 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (18 papers), Model Reduction and Neural Networks (14 papers) and Parallel Computing and Optimization Techniques (8 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (157 citations), Statistical and Nonlinear Physics (87 citations) and Fluid Flow and Transfer Processes (20 citations). Francesco Rizzi has collaborated with scholars based in United States, France and Saudi Arabia. Frequent co-authors include Bert Debusschere, Omar Knio, Maher Salloum, Reese E. Jones, Khachik Sargsyan, Habib N. Najm, Omar M. Knio, Helgi Adalsteinsson, Patrick Blonigan and Olivier Le Maı̂tre. Their work appears in journals such as The Journal of Chemical Physics, Computer Methods in Applied Mechanics and Engineering and AIAA Journal.
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.