EP-Net: Learning Cardiac Electrophysiology Models for Physiology-Based Constraints in Data-Driven Predictions. - Ibrahim Ayed; Nicolas Cedilnik; Patrick Gallinari; Maxime Sermesant (FIMH, 2019)

Learning Dynamical Systems from Partial Observations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Julien Brajard; Patrick Gallinari (CoRR, 2019)

Optimal Unsupervised Domain Translation. - Emmanuel de Bézenac; Ibrahim Ayed; Patrick Gallinari (CoRR, 2019)

Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Patrick Gallinari (ICASSP, 2020)

A Principle of Least Action for the Training of Neural Networks. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (ECML/PKDD (2), 2020)

Learning Dynamical Systems across Environments. - Yuan Yin; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (AAAI Spring Symposium: MLPS, 2021)

EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology. - Victoriya Kashtanova; Ibrahim Ayed; Nicolas Cedilnik; Patrick Gallinari; Maxime Sermesant (FIMH, 2021)

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. - Yuan Yin; Vincent Le Guen; Jérémie Donà; Emmanuel de Bézenac; Ibrahim Ayed; Nicolas Thome; Patrick Gallinari (ICLR, 2021)

LEADS: Learning Dynamical Systems that Generalize Across Environments. - Yuan Yin; Ibrahim Ayed; Emmanuel de Bézenac; Nicolas Baskiotis; Patrick Gallinari (NeurIPS, 2021)

CycleGAN Through the Lens of (Dynamical) Optimal Transport. - Emmanuel de Bézenac; Ibrahim Ayed; Patrick Gallinari (ECML/PKDD (2), 2021)

A Neural Tangent Kernel Perspective of GANs. - Jean-Yves Franceschi; Emmanuel de Bézenac; Ibrahim Ayed; Mickaël Chen; Sylvain Lamprier; Patrick Gallinari (ICML, 2022)

Deep Learning for Model Correction in Cardiac Electrophysiological Imaging. - Victoriya Kashtanova; Ibrahim Ayed; Andony Arrieula; Mark Potse; Patrick Gallinari; Maxime Sermesant (MIDL, 2022)

APHYN-EP: Physics-Based Deep Learning Framework to Learn and Forecast Cardiac Electrophysiology Dynamics. - Victoriya Kashtanova; Mihaela Pop; Ibrahim Ayed; Patrick Gallinari; Maxime Sermesant (STACOM@MICCAI, 2022)

Module-wise Training of Residual Networks via the Minimizing Movement Scheme. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (CoRR, 2022)

Modelling spatiotemporal dynamics from Earth observation data with neural differential equations. - Ibrahim Ayed; Emmanuel de Bézenac; Arthur Pajot; Patrick Gallinari (Mach. Learn., 2022)

Neural Models for Learning Real World Dynamics and the Neural Dynamics of Learning. (Réseaux de neurones profonds pour la modélisation de phénomènes physiques complexes : incorporation de connaissances a priori). - Ibrahim Ayed (2022)

Module-wise Training of Neural Networks via the Minimizing Movement Scheme. - Skander Karkar; Ibrahim Ayed; Emmanuel de Bézenac; Patrick Gallinari (NeurIPS, 2023)