Publications

(2024). Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations. npj Digital Medicine.

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(2024). A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system. Nature - Scientific Reports (in press).

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(2024). Reconstructing relaxed configurations in elastic bodies: Mathematical formulations and numerical methods for cardiac modeling. Computer Methods in Applied Mechanics and Engineering.

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(2024). An electromechanics-driven fluid dynamics model for the simulation of the whole human heart. Journal of Computational Physics.

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(2024). Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks. Nature Communications.

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(2024). Personalized pressure conditions and calibration for a predictive computational model of coronary and myocardial blood flow. Annals of Biomedical Engineering.

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(2023). lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics.

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(2023). A comprehensive mathematical model for cardiac perfusion. Nature - Scientific Reports.

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(2023). A Detailed Mathematical Model of the Human Atrial Cardiomyocyte: Integration of Electrophysiology and Cardiomechanics. The Journal of Physiology.

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(2023). Computational analysis of cardiovascular effects of COVID-19 infection in children. Journal of Pediatric Advance Research.

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(2023). A comprehensive and biophysically detailed computational model of the whole human heart electromechanics. Computer Methods in Applied Mechanics and Engineering.

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(2023). A mathematical model of the human heart suitable to address clinical problems. Japan Journal of Industrial and Applied Mathematics.

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(2022). Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains. Journal of Biomedical Engineering.

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(2022). A Novel Human Atrial Electromechanical Cardiomyocyte Model with Mechano-Calcium Feedback Effect. Computing in Cardiology.

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(2022). Physics-informed Neural Networks for parameter estimation in cardiac mechanics. ARGESIM Report.

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(2022). Neural networks based real-time simulations of cardiac electromechanics. ARGESIM Report.

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(2022). Electromechanical modeling of cardiac arrhythmias. ARGESIM Report.

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(2022). A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations. Mathematical Models and Methods in Applied Sciences.

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(2022). A cardiac electromechanical model coupled with a lumped parameters model for closed-loop blood circulation. Journal of Computational Physics.

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(2022). A machine learning method for real-time numerical simulations of cardiac electromechanics. Computer Methods in Applied Mechanics and Engineering.

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(2022). The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia. Computers in Biology and Medicine.

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(2022). Fast and robust parameter estimation with uncertainty quantification for the cardiac function. Computer Methods and Programs in Biomedicine.

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(2022). 3D-0D closed-loop model for the simulation of cardiac biventricular electromechanics. Computer Methods in Applied Mechanics and Engineering.

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(2022). Modeling the cardiac electromechanical function: a mathematical journey. Bulletin of the American Mathematical Society.

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(2021). A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes. Rendiconti Lincei - Matematica e Applicazioni.

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(2021). Electro-Mechanical Coupling in Human Atrial Cardiomyocytes: Model Development and Analysis of Inotropic Interventions. Computing in Cardiology.

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(2021). Mathematical and numerical models for the cardiac electromechanical function. Rendiconti Lincei - Matematica e Applicazioni.

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(2021). Modeling the cardiac response to hemodynamic changes associated with COVID-19: a computational study. Mathematical Biosciences and Engineering.

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(2021). Combining Data Assimilation and Machine Learning to build data-driven models for unknown long time dynamics - Applications in cardiac modeling. International Journal for Numerical Methods in Biomedical Engineering.

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(2021). An oscillation-free fully staggered algorithm for velocity-dependent active models of cardiac mechanics. Computer Methods in Applied Mechanics and Engineering.

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(2020). Biophysically detailed mathematical models of multiscale cardiac active mechanics. PLOS Computational Biology.

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(2020). Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics. Computer Methods in Applied Mechanics and Engineering.

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(2020). Metodo implementato mediante computer per la generazione di un modello matematico a complessità computazionale ridotta (patent). UIBM - Ufficio Italiano Brevetti e Marchi.

Project

(2019). Machine learning for fast and reliable solution of time-dependent differential equations. Journal of Computational Physics.

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(2018). Topology optimization of multiple anisotropic materials, with application to self-assembling diblock copolymers. Computer Methods in Applied Mechanics and Engineering.

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(2018). Active contraction of cardiac cells: a reduced model for sarcomere dynamics with cooperative interactions. Biomechanics and Modeling in Mechanobiology.

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(2018). Topology optimization with a time-integral cost functional. Finite Elements in Analysis and Design.

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(2016). Finite element approximation of a time-dependent topology optimization problem. Proceedings of ECCOMAS.

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