Physics-Informed Machine Learning

(Coming soon… :wink:)

Related Publications

  1. A.-R. Mezidi, J. Patracone and A. Habrard,
    Proximal Splitting Methods for Hybrid Differentiable Models,
    preprint, 2026.
  2. A.-R. Mezidi, J. Patracone and A. Habrard,
    Douglas-Rachford Splitting for Hybrid Differentiable Models,
    EurIPS Workshop - Differentiable Systems and Scientific Machine Learning (DiffSys), 2025.
  3. A.-R. Mezidi, J. Patracone, S. Salzo, A. Habrard, M. Pontil, R. Emonet and M. Sebban,
    A Bregman Proximal Viewpoint on Neural Operators,
    International Conference on Machine Learning (ICML), 2025.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. B. Girault, R. Emonet, A. Habrard, J. Patracone and M. Sebban,
    Erreur d’approximation pour les fonctions Sobolev regulières avec des réseaux de neurones tanh - impact théorique sur les PINNs,
    Conférence sur l'Apprentissage Automatique (CAp), 2024.

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