Davide Torlo

425 citations
24 papers · 266 · h-index 11

Impact in

    • Numerical methods for differential equations
    • Computational Fluid Dynamics and Aerodynamics
    • Advanced Numerical Methods in Computational Mathematics
    • Fluid Dynamics and Turbulent Flows

Papers in

    • Computational Fluid Dynamics and Aerodynamics 16
    • Advanced Numerical Methods in Computational Mathematics 11
    • Fluid Dynamics and Turbulent Flows 3
    • Numerical methods for differential equations 10

Davide Torlo

23 papers receiving 258 citations

Peers

Davide Torlo
Comparison fields: 5 of 28
  • Numerical Analysis 103
  • Computational Mechanics 200
  • Statistical and Nonlinear Physics 60
  • Statistics, Probability and Uncertainty 31
  • Modeling and Simulation 18
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Citations per field
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Citations per year

Countries citing papers authored by Davide Torlo

Since Specialization
Citations

This map shows the geographic impact of Davide Torlo'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 Davide Torlo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Torlo more than expected).

Fields of papers citing papers by Davide Torlo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davide Torlo. 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 Davide Torlo. The network helps show where Davide Torlo may publish in the future.

Co-authors

The 9 scholars most cited alongside Davide Torlo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Davide Torlo Line = papers co-authored together Davide Torlo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202030
2 202229
3 202124
4 202022
5 202217
6 202217
7 201817
8 201816
9 202314
10 202111
11 202211
12 202310
13 20237
14 20227
15 20237
16 20236
17 20246
18 20254
19 20224
20 20243

About Davide Torlo

Davide Torlo is a scholar working on Computational Mechanics, Numerical Analysis, Statistical and Nonlinear Physics, Atmospheric Science and Statistics, Probability and Uncertainty, having authored 24 papers that have together received 266 indexed citations. Recurring topics across this work include Computational Fluid Dynamics and Aerodynamics (16 papers), Advanced Numerical Methods in Computational Mathematics (11 papers), Numerical methods for differential equations (10 papers), Model Reduction and Neural Networks (7 papers), Probabilistic and Robust Engineering Design (3 papers), Fluid Dynamics and Turbulent Flows (3 papers), Meteorological Phenomena and Simulations (3 papers) and Numerical methods in engineering (2 papers). The work is most often cited by research in Numerical Analysis (103 citations), Computational Mechanics (200 citations), Statistical and Nonlinear Physics (60 citations), Statistics, Probability and Uncertainty (31 citations) and Modeling and Simulation (18 citations). Davide Torlo has collaborated with scholars based in Italy, Switzerland and Germany. Frequent co-authors include Philipp Öffner, Rémi Abgrall, Mario Ricchiuto, Gianluigi Rozza, Elena Gaburro, Francesco Ballarin, Svetlana Tokareva, Hendrik Ranocha and Walter Boscheri. Their work appears in journals such as Journal of Scientific Computing, Applied Numerical Mathematics, Applied Mathematics and Computation, Computers & Fluids and Mathematics and Computers in Simulation.

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.

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