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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A machine learning method for real-time numerical simulations of cardiac electromechanics
Francesco Regazzoni, Matteo Salvador, Luca Dede’, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 393, pp. 114825-114825
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

A comprehensive and biophysically detailed computational model of the whole human heart electromechanics
Marco Fedele, Roberto Piersanti, Francesco Regazzoni, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 410, pp. 115983-115983
Open Access | Times Cited: 58
An electromechanics-driven fluid dynamics model for the simulation of the whole human heart
Alberto Zingaro, Michele Bucelli, Roberto Piersanti, et al.
Journal of Computational Physics (2024) Vol. 504, pp. 112885-112885
Open Access | Times Cited: 20
life: A flexible, high performance library for the numerical solution of complex finite element problems
Pasquale Claudio Africa
SoftwareX (2022) Vol. 20, pp. 101252-101252
Open Access | Times Cited: 49
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations
Matteo Salvador, Marina Strocchi, Francesco Regazzoni, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 10
Fast and robust parameter estimation with uncertainty quantification for the cardiac function
Matteo Salvador, Francesco Regazzoni, Luca Dede’, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 231, pp. 107402-107402
Open Access | Times Cited: 18
A review on machine learning approaches in cardiac tissue engineering
Nikhith Kalkunte, Jorge Cisneros, Edward Castillo, et al.
Frontiers in Biomaterials Science (2024) Vol. 3
Open Access | Times Cited: 4
A deep learning algorithm to accelerate algebraic multigrid methods in finite element solvers of 3D elliptic PDEs
Matteo Caldana, Paola F. Antonietti, Luca Dede’
Computers & Mathematics with Applications (2024) Vol. 167, pp. 217-231
Open Access | Times Cited: 4
Digital twinning of cardiac electrophysiology for congenital heart disease
Matteo Salvador, Fanwei Kong, Mathias Peirlinck, et al.
Journal of The Royal Society Interface (2024) Vol. 21, Iss. 215
Open Access | Times Cited: 4
A new hybrid reduced order modeling for parametrized Navier–Stokes equations in stream-vorticity formulation
Tao Zhang, Hui Xu, Lei Guo, et al.
Physics of Fluids (2024) Vol. 36, Iss. 6
Closed Access | Times Cited: 3
Efficient approximation of cardiac mechanics through reduced‐order modeling with deep learning‐based operator approximation
Ludovica Cicci, Stefania Fresca, Andrea Manzoni, et al.
International Journal for Numerical Methods in Biomedical Engineering (2023) Vol. 40, Iss. 1
Open Access | Times Cited: 8
Scientific Machine Learning Enables Multiphysics Digital Twins of Large-Scale Electronic Chips
Xiao Li, Qiwei Zhan, Bozhao Sun, et al.
IEEE Transactions on Microwave Theory and Techniques (2022) Vol. 70, Iss. 12, pp. 5305-5318
Closed Access | Times Cited: 13
How drugs modulate the performance of the human heart
Mathias Peirlinck, Yao Jiang, Francisco Sahli Costabal, et al.
Computational Mechanics (2022) Vol. 69, Iss. 6, pp. 1397-1411
Open Access | Times Cited: 11
Personalized biomechanical insights in atrial fibrillation: opportunities & challenges
Åshild Telle, Clarissa Bargellini, Yaacoub Chahine, et al.
Expert Review of Cardiovascular Therapy (2023) Vol. 21, Iss. 11, pp. 817-837
Closed Access | Times Cited: 5
A mathematical model of the human heart suitable to address clinical problems
Alfio Quarteroni, Luca Dede’, Francesco Regazzoni, et al.
Japan Journal of Industrial and Applied Mathematics (2023) Vol. 40, Iss. 3, pp. 1547-1567
Closed Access | Times Cited: 4
Branched Latent Neural Maps
Matteo Salvador, Alison L. Marsden
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 418, pp. 116499-116499
Open Access | Times Cited: 4
Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance
Yili Feng, Ruisen Fu, Hao Sun, et al.
Artificial Intelligence in Medicine (2023) Vol. 147, pp. 102744-102744
Closed Access | Times Cited: 4
Simulating impaired left ventricular–arterial coupling in aging and disease: a systematic review
Corina Cheng Ai Ding, Socrates Dokos, Azam Ahmad Bakir, et al.
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 1
Residual-based reduced order models for parameterized Navier–Stokes equations with nonhomogeneous boundary condition
Tao Zhang, Hui Xu, Yan Zhang, et al.
Physics of Fluids (2024) Vol. 36, Iss. 9
Closed Access | Times Cited: 1
Interpretable machine learning of action potential duration restitution kinetics in single-cell models of atrial cardiomyocytes
Jun-Seop Song, Young-Seon Lee
Journal of Electrocardiology (2022) Vol. 74, pp. 137-145
Open Access | Times Cited: 6
An electromechanics-driven fluid dynamics model for the simulation of the whole human heart
Alberto Zingaro, Michele Bucelli, Roberto Piersanti, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 3
Modeling Transient Cardiovascular Hemodynamics With Physiological Conscious Autoencoder
Mehmet Yaşar İşcan, Aydın Yeşildirek
IEEE Access (2023) Vol. 11, pp. 111909-111926
Open Access | Times Cited: 3
Reduced order modeling of the cardiac function across the scales
Ludovica Cicci, Stefania Fresca, Elena Zappon, et al.
Elsevier eBooks (2023), pp. 403-433
Closed Access | Times Cited: 2
Computational Modelling Enabling In Silico Trials for Cardiac Physiologic Pacing
Marina Strocchi, Nadeev Wijesuriya, Vishal Mehta, et al.
Journal of Cardiovascular Translational Research (2023) Vol. 17, Iss. 3, pp. 685-694
Open Access | Times Cited: 2
On building machine learning models for medical dataset with correlated features
Debismita Nayak, Sai Lakshmi Radhika Tantravahi
Computational and Mathematical Biophysics (2024) Vol. 12, Iss. 1
Open Access
Reconstruction of the local contractility of the cardiac muscle from deficient apparent kinematics
G. Pozzi, D. Ambrosi, Simone Pezzuto
Journal of the Mechanics and Physics of Solids (2024) Vol. 192, pp. 105793-105793
Open Access
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