Francesco Regazzoni

1.3k total citations · 1 hit paper
47 papers, 795 citations indexed

About

Francesco Regazzoni is a scholar working on Cardiology and Cardiovascular Medicine, Statistical and Nonlinear Physics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Francesco Regazzoni has authored 47 papers receiving a total of 795 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Cardiology and Cardiovascular Medicine, 11 papers in Statistical and Nonlinear Physics and 7 papers in Statistics, Probability and Uncertainty. Recurrent topics in Francesco Regazzoni's work include Cardiac electrophysiology and arrhythmias (27 papers), Cardiovascular Function and Risk Factors (24 papers) and Model Reduction and Neural Networks (11 papers). Francesco Regazzoni is often cited by papers focused on Cardiac electrophysiology and arrhythmias (27 papers), Cardiovascular Function and Risk Factors (24 papers) and Model Reduction and Neural Networks (11 papers). Francesco Regazzoni collaborates with scholars based in Italy, Switzerland and United States. Francesco Regazzoni's co-authors include Alfio Quarteroni, Luca Dede’, Matteo Salvador, Marco Fedele, Pasquale Claudio Africa, Roberto Piersanti, Stefano Pagani, Christian Vergara, Alberto Zingaro and Michele Bucelli and has published in prestigious journals such as Nature Communications, The Journal of Physiology and Scientific Reports.

In The Last Decade

Francesco Regazzoni

45 papers receiving 769 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.

A comprehensive and biophysically detailed computational model of the whole human heart electromechanics

2023 71 citations Marco Fedele, Roberto Piersanti et al. Computer Methods in Applied Mechanics and Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Francesco Regazzoni Italy	18	439	182	168	100	65	47	795
Toni Lassila United Kingdom	14	259 0.6×	166 0.9×	200 1.2×	249 2.5×	65 1.0×	34	828
Matteo Salvador Italy	14	313 0.7×	129 0.7×	77 0.5×	71 0.7×	59 0.9×	30	491
Simone Pezzuto Switzerland	14	394 0.9×	195 1.1×	67 0.4×	38 0.4×	64 1.0×	41	638
Li Cai China	12	158 0.4×	139 0.8×	105 0.6×	141 1.4×	26 0.4×	78	614
Pasquale Claudio Africa Italy	13	347 0.8×	141 0.8×	56 0.3×	87 0.9×	79 1.2×	22	543
Phani Chinchapatnam United Kingdom	15	532 1.2×	148 0.8×	21 0.1×	122 1.2×	223 3.4×	31	928
Yasser Aboelkassem United States	16	195 0.4×	301 1.7×	19 0.1×	180 1.8×	27 0.4×	58	723
Radomír Chabiniok United Kingdom	15	644 1.5×	377 2.1×	49 0.3×	90 0.9×	270 4.2×	43	978
Simone Deparis Switzerland	23	347 0.8×	319 1.8×	303 1.8×	855 8.6×	66 1.0×	56	1.5k
Bodo Erdmann Germany	14	75 0.2×	336 1.8×	45 0.3×	304 3.0×	169 2.6×	39	969

Countries citing papers authored by Francesco Regazzoni

Since Specialization

Citations

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

Fields of papers citing papers by Francesco Regazzoni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Regazzoni

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

All Works

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20 of 20 papers shown

Pagani, Stefano, et al.. (2025). A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts. Computer Methods in Applied Mechanics and Engineering. 437. 117796–117796.

Regazzoni, Francesco, Matteo Salvador, Luca Dede’, et al.. (2025). Two New Calibration Techniques of Lumped‐Parameter Mathematical Models for the Cardiovascular System. International Journal for Numerical Methods in Engineering. 126(1). 2 indexed citations

Regazzoni, Francesco, Jacques M. Huyghe, Andrea Baggiano, et al.. (2024). Modeling cardiac microcirculation for the simulation of coronary flow and 3D myocardial perfusion. Biomechanics and Modeling in Mechanobiology. 23(6). 1863–1888. 3 indexed citations

Salvador, Matteo, et al.. (2024). An integrated heart–torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation. Computer Methods in Applied Mechanics and Engineering. 427. 117077–117077. 6 indexed citations

Regazzoni, Francesco, Stefano Pagani, Matteo Salvador, Luca Dede’, & Alfio Quarteroni. (2024). Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks. Nature Communications. 15(1). 1834–1834. 17 indexed citations

Bucelli, Michele, et al.. (2024). Robust Radial Basis Function Interpolation Based on Geodesic Distance for the Numerical Coupling of Multiphysics Problems. SIAM Journal on Scientific Computing. 46(6). B981–B1002.

Regazzoni, Francesco, Massimiliano Maines, Domenico Catanzariti, et al.. (2024). Personalized computational electro-mechanics simulations to optimize cardiac resynchronization therapy. Biomechanics and Modeling in Mechanobiology. 23(6). 1977–2004. 2 indexed citations

Africa, Pasquale Claudio, Roberto Piersanti, Francesco Regazzoni, et al.. (2023). lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics. 24(1). 389–389. 16 indexed citations

Salvador, Matteo, Francesco Regazzoni, Luca Dede’, & Alfio Quarteroni. (2023). Fast and robust parameter estimation with uncertainty quantification for the cardiac function. Computer Methods and Programs in Biomedicine. 231. 107402–107402. 22 indexed citations

10.

Zingaro, Alberto, Christian Vergara, Luca Dede’, Francesco Regazzoni, & Alfio Quarteroni. (2023). A comprehensive mathematical model for cardiac perfusion. Scientific Reports. 13(1). 14220–14220. 18 indexed citations

11.

Bartolucci, Chiara, Francesco Regazzoni, Michelangelo Paci, et al.. (2023). A detailed mathematical model of the human atrial cardiomyocyte: integration of electrophysiology and cardiomechanics. The Journal of Physiology. 602(18). 4543–4583. 5 indexed citations

12.

Regazzoni, Francesco, et al.. (2022). A Novel Human Atrial Electromechanical Cardiomyocyte Model with Mechano-Calcium Feedback Effect. Computing in cardiology. 1 indexed citations

13.

Regazzoni, Francesco, et al.. (2022). A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations. Mathematical Models and Methods in Applied Sciences. 32(8). 1531–1556. 10 indexed citations

14.

Dede’, Luca, Francesco Regazzoni, Christian Vergara, et al.. (2021). Modeling the cardiac response to hemodynamic changes associated with COVID-19: a computational study. Mathematical Biosciences & Engineering. 18(4). 3364–3383. 10 indexed citations

15.

Regazzoni, Francesco & Alfio Quarteroni. (2021). Accelerating the convergence to a limit cycle in 3D cardiac electromechanical simulations through a data-driven 0D emulator. Computers in Biology and Medicine. 135. 104641–104641. 19 indexed citations

16.

Regazzoni, Francesco. (2020). Mathematical modeling and machine learning for the numerical simulation of cardiac electromechanics. 9 indexed citations

17.

Regazzoni, Francesco, Luca Dede’, & Alfio Quarteroni. (2020). Active Force Generation in Cardiac Muscle Cells: Mathematical Modeling and Numerical Simulation of the Actin-Myosin Interaction. Vietnam Journal of Mathematics. 49(1). 87–118. 25 indexed citations

18.

Regazzoni, Francesco & Alfio Quarteroni. (2020). An oscillation-free fully staggered algorithm for velocity-dependent active models of cardiac mechanics. Computer Methods in Applied Mechanics and Engineering. 373. 113506–113506. 16 indexed citations

19.

Regazzoni, Francesco, Luca Dede’, & Alfio Quarteroni. (2018). Active contraction of cardiac cells: a reduced model for sarcomere dynamics with cooperative interactions. Biomechanics and Modeling in Mechanobiology. 17(6). 1663–1686. 31 indexed citations

20.

Bruggi, Matteo, Nicola Parolini, Francesco Regazzoni, & Marco Verani. (2016). FINITE ELEMENT APPROXIMATION OF A TIME-DEPENDENT TOPOLOGY OPTIMIZATION PROBLEM. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 3711–3723. 2 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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