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Rapport Année : 2023

Physics-Informed Neural Networks for Multiphysics Coupling: Application to Conjugate Heat Transfer

Résumé

Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for modeling complex physical phenomena, offering the potential to handle diverse scenarios to simulate coupled systems. This is a supervised or unsupervised deep learning approach that aims at learning physical laws described by partial differential equations. This report presents an exploration of PINNs through three distinct test cases: heat transfer, and conjugate heat transfer, with forced and natural convection. The investigations reveal PINNs' proficiency in accommodating parameterized resolution, addressing piece-wise constant conditions, and enabling multiphysics coupling. Despite their versatility, challenges emerged, including difficulties in achieving high accuracy, error propagation near singularities, and limitations in scenarios with high Rayleigh values.
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Dates et versions

hal-04225990 , version 1 (05-10-2023)

Licence

Domaine public

Identifiants

  • HAL Id : hal-04225990 , version 1

Citer

Guillaume Coulaud, Régis Duvigneau. Physics-Informed Neural Networks for Multiphysics Coupling: Application to Conjugate Heat Transfer. RR-9520, Université Côte d'Azur, Inria, CNRS, LJAD. 2023. ⟨hal-04225990⟩
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