Publications related to Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive me ...

Aip Publishing2024

The role of computational methods in cardiovascular medicine: a narrative review

Alfio Quarteroni, Luca Dede', Christian Vergara, Stefano Pagani

Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

Ame Publishing Company2024

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Alfio Quarteroni, Francesco Regazzoni, Luca Dede'

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-b ...

Nature Portfolio2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Marco Picasso, Alexandre Caboussat, Maude Girardin

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Lausanne2024

Apparatus for non-invasively computing cardio-vasculature parameters using morphology of uncalibrated pressure wave signal

Nikolaos Stergiopoulos, Georgios Rovas, Vasiliki Bikia

Systems and apparatus are provided for using a non-invasively monitored uncalibrated pressure signal from a patient to compute cardiac output and other cardio-vascular parameters, thereby providing safe, effective, reliable and low cost monitoring of patie ...

2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani, Marco Fedele

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

London2023

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Alfio Quarteroni, Luca Dede'

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our mod ...

WILEY2023

Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

Olga Fink, Vinay Sharma, Manav Manav

We propose a physics-informed message-passing graph neural network (GNN) for learning the dynamics of springmass systems. The proposed method embeds the underlying physics directly into the message-passing scheme of the GNN. We compare the new scheme with ...

Research Publishing2023

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning.However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

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Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period

The role of computational methods in cardiovascular medicine: a narrative review

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Apparatus for non-invasively computing cardio-vasculature parameters using morphology of uncalibrated pressure wave signal

Efficient Continual Finite-Sum Minimization

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

The role of computational methods in cardiovascular medicine: a narrative review

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Apparatus for non-invasively computing cardio-vasculature parameters using morphology of uncalibrated pressure wave signal

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Efficient Continual Finite-Sum Minimization

Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.