Luca Guastoni
- Computational Mechanics top 5%
- Statistical and Nonlinear Physics top 2%
- Aerospace Engineering top 10%
- Mechanical Engineering
- Atmospheric Science
- Co-authors
- Philipp SchlatterRicardo VinuesaHossein AzizpourAndrea IaniroStefano DiscettiAlejandro GüemesJean RabaultHamidreza Eivazi
- Topics
- Fluid Dynamics and Turbulent Flows (10 papers)Model Reduction and Neural Networks (10 papers)Fluid Dynamics and Vibration Analysis (4 papers)
In The Last Decade
Luca Guastoni
9 papers receiving 415 citations
Hit Papers
Peers
Comparison fields: 5 of 49
- Computational Mechanics 329
- Statistical and Nonlinear Physics 263
- Aerospace Engineering 110
- Mechanical Engineering 63
- Atmospheric Science 55
Countries citing papers authored by Luca Guastoni
This map shows the geographic impact of Luca Guastoni'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 Luca Guastoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Guastoni more than expected).
Fields of papers citing papers by Luca Guastoni
This network shows the impact of papers produced by Luca Guastoni. 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 Luca Guastoni. The network helps show where Luca Guastoni may publish in the future.
Co-authorship network of co-authors of Luca Guastoni
This figure shows the co-authorship network connecting the top 25 collaborators of Luca Guastoni. A scholar is included among the top collaborators of Luca Guastoni 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 Luca Guastoni. Luca Guastoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 68 | |
| 7 | 8 | |
| 8 | 17 | |
| 9 | Deep Reinforcement Learning for Active Drag Reduction in Wall Turbulence | 1 |
| 10 | Convolutional-network models to predict wall-bounded turbulence from wall quantitiesbreakdown → | 144 |
| 11 | On the use of recurrent neural networks for predictions of turbulent flows | 1 |
| 12 | 187 |
About Luca Guastoni
Luca Guastoni is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Aerospace Engineering, having authored 12 papers that have together received 432 indexed citations. Recurring topics across this work include Fluid Dynamics and Turbulent Flows (10 papers), Model Reduction and Neural Networks (10 papers) and Fluid Dynamics and Vibration Analysis (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (263 citations), Computational Mechanics (329 citations) and Aerospace Engineering (110 citations). Luca Guastoni has collaborated with scholars based in Sweden, Germany and Spain. Frequent co-authors include Philipp Schlatter, Ricardo Vinuesa, Hossein Azizpour, Andrea Ianiro, Stefano Discetti, Alejandro Güemes, Jean Rabault, Hamidreza Eivazi, Alfredo Pinelli and Yuning Wang. Their work appears in journals such as Journal of Fluid Mechanics, Journal of Computational Physics and International Journal of Heat and Fluid Flow.
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.