STAGE Domain adaptation with Physics-informed neural networks

Date de mise à jour de l’offre

ÉCOLE NORMALE SUPERIEURE PARIS-SACLAY :

L'École normale supérieure Paris-Saclay est une institution universitaire française d’enseignement supérieur et de recherche, établissement-composante de l'université Paris-Saclay, située sur le plateau de Saclay, dans l'Essonne.

Description de la mission

Context:
For decades, statistical or machine learning methods have been already applied successfully in many scientific and technical fields. However, their application to engineering science remain limited because of the lack of data and the difficulty in incorporating physical knowledge of systems into the models. Considering small data regime, researchers and engineers are turning more and more into hybrid models that combines approaches based on the knowledge of physics of the systems and also the data.
Recently, there has been a growing number of researches using deep learning methods to study physical systems, which are often described by partial differential equations (PDEs), by enforcing physical constraints into models. In this work, we focus on Physics-informed neural networks (PINNs) and their variants, which are recently proposed methods for solving forward and inverse problems involving PDEs and has gained remarkable results in different fields of research since the last two years.

Mission:
The main objective of this internship is to investigate in the use of PINNs on problems with geometric variations. In the first stage of the internship, the student will study the state-of-the-art of PINNs and existing methods concerned about domain adaptation. In the second stage, the student will develop appropriate approach for PINNs using domain adaptation methods to tackle the problems with geometric variations. In the final stage, the developed method will be applied to industrial use cases.

Profil recherché

Required knowledge: deep learning (neural networks, transfer learning), numerical schemes for solving PDEs.
Programming language: Python
Formation: M2 student or gap year student M1, M2 in applied mathematics.

Niveau de qualification requis

Bac + 4/5 et +
  • Employeur
    ÉCOLE NORMALE SUPERIEURE PARIS-SACLAY
  • Secteur d’activité de la structure
    Enseignement - Formation - Recherche
  • Effectif de la structure
    Plus de 250 salariés
  • Type de stage ou contrat
    Stage d'immersion en milieu professionnel dans le cadre de la formation professionnelle continue
  • Date prévisionnelle de démarrage
  • Durée du stage ou contrat
    Plus de 4 mois et jusqu'à 6 mois
  • Le stage est-il rémunéré ?
    Oui
  • Niveau de qualification requis

    Bac + 4/5 et +
  • Lieu du stage
    ENS Paris-Saclay
    4 Avenue des Sciences
    91190 GIF SUR YVETTE
  • Accès et transports
    RER B (Saint-Rémy-lès-Chevreuse) ou RER C (Massy- Palaiseau) Arrêt : « Massy-Palaiseau » Puis prendre un des bus suivants :- Bus 91.06C (Christ de Saclay) - Bus 91.06B (Saint-Quentin-en-Yvelines)