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

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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

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

Graph Chatbot

Chattez avec Graph Search

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

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

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

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations