Luca Guastoni

661 total citations · 1 hit paper
12 papers, 432 citations indexed

About

Luca Guastoni is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Aerospace Engineering. According to data from OpenAlex, Luca Guastoni has authored 12 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Mechanics, 10 papers in Statistical and Nonlinear Physics and 3 papers in Aerospace Engineering. Recurrent topics in Luca Guastoni's work include Fluid Dynamics and Turbulent Flows (10 papers), Model Reduction and Neural Networks (10 papers) and Fluid Dynamics and Vibration Analysis (4 papers). Luca Guastoni is often cited by papers focused on Fluid Dynamics and Turbulent Flows (10 papers), Model Reduction and Neural Networks (10 papers) and Fluid Dynamics and Vibration Analysis (4 papers). Luca Guastoni collaborates with scholars based in Sweden, Germany and Spain. Luca Guastoni's co-authors include Philipp Schlatter, Ricardo Vinuesa, Hossein Azizpour, Andrea Ianiro, Stefano Discetti, Alejandro Güemes, Jean Rabault, Hamidreza Eivazi, Alfredo Pinelli and Yuning Wang and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Computational Physics and International Journal of Heat and Fluid Flow.

In The Last Decade

Luca Guastoni

9 papers receiving 415 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Convolutional-network models to predict wall-bounded turbulence from wall quantities

2021 144 citations Luca Guastoni, Alejandro Güemes et al. Journal of Fluid Mechanics profile →

Peers

Luca Guastoni

CM

SNP

AE

ME

AS

Linyang Zhu China

Alejandro Güemes Spain

Mustafa Z. Yousif South Korea

Linqi Yu South Korea

Zelong Yuan China

Carlos Sanmiguel Vila Spain

Yiyang Sun United States

Miguel Alfonso Mendez Belgium

Sk. Mashfiqur Rahman United States

Andrea Mola Italy

Linyang Zhu China

Luca Guastoni

353 ×1.1

CM

277 ×1.1

SNP

191 ×1.7

AE

56 ×0.9

ME

33 ×0.6

AS

Citations per year, relative to Luca Guastoni Luca Guastoni (= 1×) peers Linyang Zhu

Countries citing papers authored by Luca Guastoni

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Wei, Hao, et al.. (2025). PICT–A differentiable, GPU-accelerated multi-block PISO solver for simulation-coupled learning tasks in fluid dynamics. Journal of Computational Physics. 544. 114433–114433.

2.

Wang, Yuning, et al.. (2025). Easy attention: A simple attention mechanism for temporal predictions with transformers. 1(1). 16104–16104.

3.

Guastoni, Luca, et al.. (2024). Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows. Flow Turbulence and Combustion. 115(3). 1291–1317. 2 indexed citations

4.

Guastoni, Luca, Alejandro Güemes, Andrea Ianiro, et al.. (2024). Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers. Theoretical and Computational Fluid Dynamics. 39(1). 13–13. 1 indexed citations

5.

Guastoni, Luca, et al.. (2023). Direct numerical simulation of a zero-pressure-gradient turbulent boundary layer with passive scalars up to Prandtl number Pr = 6. Journal of Fluid Mechanics. 974. 3 indexed citations

6.

Guastoni, Luca, Jean Rabault, Philipp Schlatter, Hossein Azizpour, & Ricardo Vinuesa. (2023). Deep reinforcement learning for turbulent drag reduction in channel flows. The European Physical Journal E. 46(4). 27–27. 68 indexed citations

7.

Guastoni, Luca, et al.. (2023). Predicting the wall-shear stress and wall pressure through convolutional neural networks. International Journal of Heat and Fluid Flow. 103. 109200–109200. 8 indexed citations

8.

Guastoni, Luca, et al.. (2022). Predicting the temporal dynamics of turbulent channels through deep learning. International Journal of Heat and Fluid Flow. 96. 109010–109010. 17 indexed citations

9.

Guastoni, Luca, et al.. (2021). Deep Reinforcement Learning for Active Drag Reduction in Wall Turbulence. Bulletin of the American Physical Society. 1 indexed citations

10.

Guastoni, Luca, Alejandro Güemes, Andrea Ianiro, et al.. (2021). Convolutional-network models to predict wall-bounded turbulence from wall quantities. Journal of Fluid Mechanics. 928. 144 indexed citations breakdown →

11.

Guastoni, Luca, et al.. (2020). On the use of recurrent neural networks for predictions of turbulent flows. 1 indexed citations

12.

Guastoni, Luca, et al.. (2019). Predictions of turbulent shear flows using deep neural networks. Physical Review Fluids. 4(5). 187 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.

Explore authors with similar magnitude of impact