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

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Publications associées à Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation | EPFL Graph Search

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

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

Graph Chatbot

Chattez avec Graph Search

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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

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

Efficient Continual Finite-Sum Minimization

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

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

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

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

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

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.