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

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

Alfio Quarteroni, Francesco Regazzoni

When generating in-silico clinical electrophysiological outputs, such as electrocardiograms (ECGs) and body surface potential maps (BSPMs), mathematical models have relied on single physics, i.e. of the cardiac electrophysiology (EP), neglecting the role o ...

Elsevier Science Sa2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Paride Passelli

The goal of this work is to use anisotropic adaptive finite elements for the numerical simulation of aluminium electrolysis. The anisotropic adaptive criteria are based on a posteriori error estimates derived for simplified problems. First, we consider an ...

EPFL2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Lausanne2024

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

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

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Residual-based attention in physics-informed neural networks

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

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

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

An integrated heart-torso electromechanical model for the simulation of electrophysiological outputs accounting for myocardial deformation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Residual-based attention in physics-informed neural networks