Ibrahim Ayed

Sorbonne Université — France

Physics and Astronomy · Physical Sciences

5h-index211citations24works0.02yr avg
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Research Topics

Model Reduction and Neural Networks(12), Cardiac electrophysiology and arrhythmias(6), Neural Networks and Applications(6), Gaussian Processes and Bayesian Inference(4), Generative Adversarial Networks and Image Synthesis(3)

Publications24 total

Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning
Interface Focus·2023· 5 citedOpen Access
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
arXiv (Cornell University)·2023Open Access
data
Figshare·2023Open Access
Neural Models for Learning Real World Dynamics and the Neural Dynamics of Learning
theses.fr (ABES)·2022Open Access
Block-wise Training of Residual Networks via the Minimizing Movement Scheme
arXiv (Cornell University)·2022Open Access
APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics
HAL (Le Centre pour la Communication Scientifique Directe)·2022Open Access
Deep Learning for Model Correction in Cardiac Electrophysiological Imaging
HAL (Le Centre pour la Communication Scientifique Directe)·2022· 1 citedOpen Access
Modelling spatiotemporal dynamics from Earth observation data with neural differential equations
Machine Learning·2022· 3 citedOpen Access
APHYN-EP: Physics-Based Deep Learning Framework to Learn and Forecast Cardiac Electrophysiology Dynamics
Lecture notes in computer science·2022· 4 cited
Augmenting physical models with deep networks for complex dynamics forecasting*
Journal of Statistical Mechanics Theory and Experiment·2021· 92 citedOpen Access
LEADS: Learning Dynamical Systems that Generalize Across Environments
HAL (Le Centre pour la Communication Scientifique Directe)·2021Open Access
A Neural Tangent Kernel Perspective of GANs
arXiv (Cornell University)·2021Open Access
A Neural Tangent Kernel Perspective of GANs
arXiv (Cornell University)·2021· 3 citedOpen Access
LEADS: Learning Dynamical Systems that Generalize Across Environments
arXiv (Cornell University)·2021· 5 citedOpen Access
Learning Dynamical Systems across Environments.
National Conference on Artificial Intelligence·2021
EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology
Lecture notes in computer science·2021· 7 citedOpen Access
CycleGAN Through the Lens of (Dynamical) Optimal Transport
Lecture notes in computer science·2021· 4 cited
Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations
2020· 5 cited
EP-Net: Learning Cardiac Electrophysiology Models for Physiology-based Constraints in Data-Driven Predictions
HAL (Le Centre pour la Communication Scientifique Directe)·2019Open Access
Optimal Unsupervised Domain Translation
arXiv (Cornell University)·2019· 13 citedOpen Access
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Frequent Co-authors

Emmanuel de Bézenac(14), Patrick Gallinari(14), Maxime Sermesant(8), Patrick Gallinari(5), Yuan Yin(4), Arthur Pajot(4), Victoriya Kashtanova(4), Mihaela Pop(4), Nicolas Cedilnik(3), Benjamin Piwowarski(3), Victoriya Kashtanova(2), Nicolas Baskiotis(2), Jean-Yves Franceschi(2), Mickaël Chen(2), Sylvain Lamprier(2), Skander Karkar(2), Vincent Le Guen(1), Jérémie Donà(1), Nicolas Thome(1), Julien Brajard(1)