Alejandro Güemes
Impact in
-
- Model Reduction and Neural Networks
- Computational Mechanics top 5%
- Fluid Dynamics and Turbulent Flows
- Fluid Dynamics and Vibration Analysis
Papers in
-
- Fluid Dynamics and Turbulent Flows 11
- Fluid Dynamics and Vibration Analysis 4
-
- Model Reduction and Neural Networks 7
- Co-authors
- Stefano Discetti (11 shared papers)Andrea Ianiro (6 shared papers)Ricardo Vinuesa (4 shared papers)Philipp Schlatter (3 shared papers)Hossein Azizpour (2 shared papers)Luca Guastoni (2 shared papers)Carlos Sanmiguel Vila (5 shared papers)Álvaro Moreno Soto (1 shared paper)
In The Last Decade
Alejandro Güemes
13 papers receiving 372 citations
Alejandro Güemes's Hit Papers
Peers
Comparison fields: 5 of 45
- Statistical and Nonlinear Physics 209
- Computational Mechanics 281
- Aerospace Engineering 101
- Environmental Engineering 50
- Computer Vision and Pattern Recognition 61
Countries citing papers authored by Alejandro Güemes
This map shows the geographic impact of Alejandro Güemes'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 Alejandro Güemes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alejandro Güemes more than expected).
Fields of papers citing papers by Alejandro Güemes
This network shows the impact of papers produced by Alejandro Güemes. 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 Alejandro Güemes. The network helps show where Alejandro Güemes may publish in the future.
Co-authors
The 12 scholars most cited alongside Alejandro Güemes, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Convolutional-network models to predict wall-bounded turbulence from wall quantities Hit paper breakdown → | 2021 | 148 |
| 2 | Sensing coherent structures from the wall | 2021 | 107 |
| 3 | 2019 | 40 | |
| 4 | 2022 | 36 | |
| 5 | 2024 | 13 | |
| 6 | 2023 | 11 | |
| 7 | 2019 | 7 | |
| 8 | 2021 | 6 | |
| 9 | 2023 | 5 | |
| 10 | 2021 | 5 | |
| 11 | 2024 | 3 | |
| 12 | 2022 | 3 | |
| 13 | 2021 | 1 |
About Alejandro Güemes
Alejandro Güemes is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Aerospace Engineering and Environmental Engineering, having authored 13 papers that have together received 385 indexed citations. Recurring topics across this work include Fluid Dynamics and Turbulent Flows (11 papers), Model Reduction and Neural Networks (7 papers), Fluid Dynamics and Vibration Analysis (4 papers), Advanced Image Processing Techniques (4 papers), Wind and Air Flow Studies (3 papers), Heat Transfer Mechanisms (3 papers), Aerodynamics and Acoustics in Jet Flows (2 papers) and Aerodynamics and Fluid Dynamics Research (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (209 citations), Computational Mechanics (281 citations), Aerospace Engineering (101 citations), Environmental Engineering (50 citations) and Computer Vision and Pattern Recognition (61 citations). Alejandro Güemes has collaborated with scholars based in Spain, Sweden and Germany. Frequent co-authors include Stefano Discetti, Andrea Ianiro, Ricardo Vinuesa, Philipp Schlatter, Hossein Azizpour, Luca Guastoni, Carlos Sanmiguel Vila, Álvaro Moreno Soto, Marco Raiola and P. Fajardo. Their work appears in journals such as Journal of Fluid Mechanics, Measurement Science and Technology, Experimental Thermal and Fluid Science, Computer Methods in Applied Mechanics and Engineering and Theoretical and Computational Fluid Dynamics.
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.