Physics-Informed Machine Learning

(Coming soon… :wink:)

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.
  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.
  3. 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.
  4. 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.

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