Francesco Ballarin

1.8k total citations
54 papers, 909 citations indexed

About

Francesco Ballarin is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Francesco Ballarin has authored 54 papers receiving a total of 909 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistical and Nonlinear Physics, 36 papers in Computational Mechanics and 9 papers in Statistics, Probability and Uncertainty. Recurrent topics in Francesco Ballarin's work include Model Reduction and Neural Networks (37 papers), Advanced Numerical Methods in Computational Mathematics (19 papers) and Probabilistic and Robust Engineering Design (9 papers). Francesco Ballarin is often cited by papers focused on Model Reduction and Neural Networks (37 papers), Advanced Numerical Methods in Computational Mathematics (19 papers) and Probabilistic and Robust Engineering Design (9 papers). Francesco Ballarin collaborates with scholars based in Italy, United States and Switzerland. Francesco Ballarin's co-authors include Gianluigi Rozza, Andrea Manzoni, Alfio Quarteroni, Teeratorn Kadeethum, Sanghyun Lee, Hamidreza M. Nick, Nikolaos Bouklas, Roberto Scrofani, Elena Faggiano and Sonia Ippolito and has published in prestigious journals such as Scientific Reports, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Francesco Ballarin

50 papers receiving 887 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Francesco Ballarin Italy 17 618 524 199 110 100 54 909
Dominique Pelletier Canada 18 194 0.3× 929 1.8× 146 0.7× 111 1.0× 127 1.3× 92 1.1k
Giovanni Stabile Italy 15 523 0.8× 460 0.9× 182 0.9× 68 0.6× 53 0.5× 43 775
Qizhi He United States 16 394 0.6× 189 0.4× 93 0.5× 145 1.3× 267 2.7× 39 1.0k
Pedro Dı́ez Spain 21 475 0.8× 761 1.5× 268 1.3× 126 1.1× 591 5.9× 111 1.5k
Gene Hou United States 17 81 0.1× 754 1.4× 258 1.3× 121 1.1× 191 1.9× 73 1.3k
Jean‐Sébastien Schotté France 9 95 0.2× 812 1.5× 66 0.3× 127 1.2× 412 4.1× 16 1.3k
I. Alfaro Spain 6 220 0.4× 144 0.3× 106 0.5× 81 0.7× 146 1.5× 7 475
M. Damodaran Singapore 11 315 0.5× 386 0.7× 177 0.9× 94 0.9× 41 0.4× 51 694
Elie Hachem France 19 360 0.6× 925 1.8× 20 0.1× 214 1.9× 146 1.5× 97 1.4k
Mengwu Guo Netherlands 11 477 0.8× 213 0.4× 279 1.4× 99 0.9× 81 0.8× 22 746

Countries citing papers authored by Francesco Ballarin

Since Specialization
Citations

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

Fields of papers citing papers by Francesco Ballarin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Ballarin

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Ballarin. A scholar is included among the top collaborators of Francesco Ballarin 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 Francesco Ballarin. Francesco Ballarin 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.
Ballarin, Francesco, et al.. (2025). Variational multiscale evolve and filter strategies for convection-dominated flows. Computer Methods in Applied Mechanics and Engineering. 438. 117811–117811. 1 indexed citations
2.
Africa, Pasquale Claudio, et al.. (2025). Projection-based reduced order modelling for unsteady parametrized optimal control problems in 3D cardiovascular flows. Computer Methods and Programs in Biomedicine. 269. 108813–108813.
3.
Zyrianoff, Ivan, Stefano Borgo, Claudio Masolo, et al.. (2024). A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics. Applied Sciences. 14(24). 11489–11489. 3 indexed citations
4.
Rozza, Gianluigi, et al.. (2024). Real Time Reduced Order Computational Mechanics. 6 indexed citations
5.
Ballarin, Francesco, et al.. (2023). An artificial neural network approach to bifurcating phenomena in computational fluid dynamics. Computers & Fluids. 254. 105813–105813. 45 indexed citations
6.
Ballarin, Francesco, et al.. (2023). Certified Reduced Basis VMS-Smagorinsky model for natural convection ow in a cavity with variable height. arXiv (Cornell University). 8 indexed citations
7.
Girfoglio, Michele, et al.. (2023). Data-Driven Reduced Order Modelling for Patient-Specific Hemodynamics of Coronary Artery Bypass Grafts with Physical and Geometrical Parameters. Journal of Scientific Computing. 94(2). 16 indexed citations
8.
Ali, Shafqat, et al.. (2023). A reduced basis stabilization for the unsteady Stokes and Navier-Stokes equations. arXiv (Cornell University). 1(2). 180–201. 3 indexed citations
9.
Ballarin, Francesco, et al.. (2023). Model order reduction for deforming domain problems in a time‐continuous space‐time setting. International Journal for Numerical Methods in Engineering. 124(23). 5125–5150.
10.
Kadeethum, Teeratorn, Francesco Ballarin, Daniel O’Malley, et al.. (2022). Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning. Scientific Reports. 12(1). 20654–20654. 13 indexed citations
11.
Kadeethum, Teeratorn, Daniel O’Malley, Francesco Ballarin, et al.. (2022). Enhancing high-fidelity nonlinear solver with reduced order model. Scientific Reports. 12(1). 20229–20229. 10 indexed citations
12.
Ballarin, Francesco, et al.. (2021). Hierarchical model reduction techniques for flow modeling in a parametrized setting. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2 indexed citations
13.
Ballarin, Francesco, et al.. (2021). . arXiv (Cornell University). 15 indexed citations
14.
Ballarin, Francesco, et al.. (2021). A Reduced Order Cut Finite Element method for geometrically parametrized steady and unsteady Navier–Stokes problems. Computers & Mathematics with Applications. 116. 140–160. 5 indexed citations
15.
16.
Ali, Shafqat, Francesco Ballarin, & Gianluigi Rozza. (2020). Stabilized reduced basis methods for parametrized steady Stokes and Navier-Stokes equations. arXiv (Cornell University). 20 indexed citations
17.
Ballarin, Francesco, et al.. (2020). Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient‐specific data assimilation. International Journal for Numerical Methods in Biomedical Engineering. 37(12). e3367–e3367. 17 indexed citations
18.
Ballarin, Francesco, et al.. (2019). A POD-selective inverse distance weighting method for fast parametrized shape morphing. arXiv (Cornell University). 30 indexed citations
19.
Ballarin, Francesco, Elena Faggiano, Andrea Manzoni, et al.. (2017). Numerical modeling of hemodynamics scenarios of patient-specific coronary artery bypass grafts. Biomechanics and Modeling in Mechanobiology. 16(4). 1373–1399. 29 indexed citations
20.
Ballarin, Francesco, Andrea Manzoni, Alfio Quarteroni, & Gianluigi Rozza. (2015). Supremizer stabilization of POD-Galerkin approximation of parametrized Navier-Stokes equations. 71(1). 3 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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