Angelo Iollo

2.5k total citations
102 papers, 1.7k citations indexed

About

Angelo Iollo is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Applied Mathematics. According to data from OpenAlex, Angelo Iollo has authored 102 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Computational Mechanics, 30 papers in Statistical and Nonlinear Physics and 24 papers in Applied Mathematics. Recurrent topics in Angelo Iollo's work include Fluid Dynamics and Turbulent Flows (44 papers), Computational Fluid Dynamics and Aerodynamics (42 papers) and Model Reduction and Neural Networks (29 papers). Angelo Iollo is often cited by papers focused on Fluid Dynamics and Turbulent Flows (44 papers), Computational Fluid Dynamics and Aerodynamics (42 papers) and Model Reduction and Neural Networks (29 papers). Angelo Iollo collaborates with scholars based in France, Italy and United States. Angelo Iollo's co-authors include Michel Bergmann, Charles‐Henri Bruneau, Stéphane Lanteri, Jean‐Antoine Désidéri, Luca Zannetti, Simone Camarri, Manuel D. Salas, Damiano Lombardi, Maria Vittoria Salvetti and Florian Bernard and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Angelo Iollo

95 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Angelo Iollo France 22 1.3k 803 401 280 160 102 1.7k
Krzysztof Fidkowski United States 21 2.3k 1.8× 564 0.7× 260 0.6× 172 0.6× 248 1.6× 111 2.5k
Todd Oliver United States 16 771 0.6× 245 0.3× 246 0.6× 253 0.9× 82 0.5× 39 1.1k
Jason E. Hicken United States 18 1.3k 1.0× 210 0.3× 330 0.8× 198 0.7× 203 1.3× 72 1.7k
Charles‐Henri Bruneau France 15 1.4k 1.0× 324 0.4× 264 0.7× 85 0.3× 34 0.2× 39 1.6k
Zhu Wang United States 23 931 0.7× 884 1.1× 137 0.3× 356 1.3× 21 0.1× 91 1.5k
John T. Batina United States 27 2.5k 1.9× 270 0.3× 1.1k 2.8× 189 0.7× 310 1.9× 82 2.9k
Oktay Baysal United States 21 1.2k 0.9× 161 0.2× 685 1.7× 125 0.4× 268 1.7× 126 1.7k
Roel Verstappen Netherlands 17 1.3k 1.0× 246 0.3× 293 0.7× 63 0.2× 73 0.5× 56 1.5k
Jean‐Yves Trépanier Canada 23 1.3k 1.0× 99 0.1× 414 1.0× 115 0.4× 159 1.0× 122 1.8k
Leo G. Rebholz United States 26 1.7k 1.3× 434 0.5× 67 0.2× 109 0.4× 183 1.1× 125 2.1k

Countries citing papers authored by Angelo Iollo

Since Specialization
Citations

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

Fields of papers citing papers by Angelo Iollo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Angelo Iollo

This figure shows the co-authorship network connecting the top 25 collaborators of Angelo Iollo. A scholar is included among the top collaborators of Angelo Iollo 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 Angelo Iollo. Angelo Iollo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fabbri, Francesco�, et al.. (2025). Graph-Convolutional-Beta-VAE for synthetic abdominal aortic aneurysm generation. Medical & Biological Engineering & Computing.
2.
3.
Bergmann, Michel, et al.. (2024). High Order ADER-IPDG Methods for the Unsteady Advection-Diffusion Equation. Communications on Applied Mathematics and Computation. 6(3). 1954–1977.
4.
Bergmann, Michel, et al.. (2021). The DGDD method for reduced-order modeling of conservation laws. Journal of Computational Physics. 437. 110336–110336. 15 indexed citations
5.
Ferrero, Andrea, et al.. (2020). Field inversion for data-augmented RANS modelling in turbomachinery flows. Computers & Fluids. 201. 104474–104474. 28 indexed citations
6.
Iollo, Angelo, et al.. (2019). An asymptotic-preserving all-speed scheme for fluid dynamics and nonlinear elasticity. HAL (Le Centre pour la Communication Scientifique Directe). 10 indexed citations
7.
Iollo, Angelo, et al.. (2019). An Implicit Scheme for Moving Walls and Multi-Material Interfaces in Weakly Compressible Materials. Communications in Computational Physics. 27(1). 116–144. 1 indexed citations
8.
Iollo, Angelo, et al.. (2017). A Cartesian Scheme for Compressible Multimaterial Hyperelastic Models with Plasticity. Communications in Computational Physics. 22(5). 1362–1384. 17 indexed citations
9.
Iollo, Angelo & Damiano Lombardi. (2014). Advection modes by optimal mass transfer. Physical Review E. 89(2). 22923–22923. 57 indexed citations
10.
Iollo, Angelo, et al.. (2014). A simple Cartesian scheme for compressible multimaterials. Journal of Computational Physics. 272. 772–798. 21 indexed citations
11.
Colin, Thierry, et al.. (2011). SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS. Mathematical Models and Methods in Applied Sciences. 22(6). 13 indexed citations
12.
Bergmann, Michel & Angelo Iollo. (2008). Improving Proper Orthogonal Decomposition Robustness by Optimal Sampling. Bulletin of the American Physical Society. 61(1). 51–5. 1 indexed citations
13.
Chernyshenko, S. I., Gaetano Maria Di Cicca, Angelo Iollo, et al.. (2006). Analysis of data on the relation between eddies and streaky structures in turbulent flows using the placebo method. Fluid Dynamics. 41(5). 772–783. 5 indexed citations
14.
Ferlauto, Michele, et al.. (2005). Blade Camber Surface Optimization for Turbomachinery Design. PORTO Publications Open Repository TOrino (Politecnico di Torino). 2 indexed citations
15.
D’Ambrosio, Domenic, et al.. (2003). The continuous adjoint to the Navier-Stokes equations and its application to hybrid evolutionary airfoil design. PORTO Publications Open Repository TOrino (Politecnico di Torino). 2 indexed citations
16.
Iollo, Angelo, Alain Dervieux, Stéphane Lanteri, & Jean‐Antoine Désidéri. (2000). TWO STABLE POD-BASED APPROXIMATION TO THE NAVIER'STOKES EQUATIONS. PORTO Publications Open Repository TOrino (Politecnico di Torino). 2 indexed citations
17.
Ferlauto, Michele, Angelo Iollo, & Luca Zannetti. (2000). Optimal Inverse Method for Turbomachinery Design. PORTO Publications Open Repository TOrino (Politecnico di Torino). 1 indexed citations
18.
Iollo, Angelo & Luca Zannetti. (2000). Optimal Control of a Vortex Trapped by an Airfoil with a Cavity. Flow Turbulence and Combustion. 65(3-4). 417–430. 8 indexed citations
19.
Zannetti, Luca & Angelo Iollo. (1999). On the Circulation Manifold for Two Adjacent Lifting Sections. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 79(10). 685–692.
20.
Salas, Manuel D. & Angelo Iollo. (1996). Entropy jump across an inviscid shock wave. Theoretical and Computational Fluid Dynamics. 8(5). 365–375. 19 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.

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