Physics-Informed Machine Learning

Learning physical problems and incorporating/unveiling background knowledge

(Coming soon… )

Related Publications

1. A.-R. Mezidi, R. Emonet, J. Patracone, A. Habrard, S. Salzo and M. Sebban,

   A Bilevel Optimization Framework for Training Bregman Neural Operators,

   Workshop - Fondements Mathématiques de l'IA, 2024.
   Article Page   PDF   BibTeX  
2. A.-R. Mezidi, R. Emonet, J. Patracone, A. Habrard, S. Salzo and M. Sebban,

   Bregman Neural Operators for Predicting Fluid Dynamics,

   Workshop - Machine Learning for Fluid Dynamics, 2024.
   Article Page   PDF   BibTeX  
3. A.-R. Mezidi, R. Emonet, J. Patracone, A. Habrard, M. Pontil, S. Salzo and M. Sebban,

   Bregman Fourier Neural Operators,

   Conférence sur l'Apprentissage Automatique (CAp), 2024.
   Article Page   PDF   BibTeX  
4. A.-R. Mezidi, J. Patracone, S. Salzo, A. Habrard, M. Pontil, R. Emonet and M. Sebban,

   Bregman Proximal Viewpoint on Neural Operators,

   Under review, 2024.
   Article Page   PDF   BibTeX  
5. B. Girault, R. Emonet, A. Habrard, J. Patracone and M. Sebban,

   Approximation Error of Sobolev Regular Functions with tanh Neural Networks - Theoretical Impact on PINNs,

   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2024.
   Article Page   PDF   BibTeX  
6. B. Girault, R. Emonet, A. Habrard, J. Patracone and M. Sebban,

   Erreur d’approximation pour les fonctions Sobolev regulières avec des réseaux de reurones tanh - impact théorique sur les PINNs,

   Conférence sur l'Apprentissage Automatique (CAp), 2024.
   Article Page   PDF   BibTeX  

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