General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
SL-DES-24-0158
Thesis topic details
Category
Engineering science
Thesis topics
Benefits of Physics-Informed Neural Networks methods for complex problems in physics applications
Contract
Thèse
Job description
Strategies for approximating the solutions of partial differential equations by means of Neural Networks have recently gained in popularity. These so-called Physics-Informed Neural Networks (PINNs) are issued from recent advances in the field of Artificial Intelligence and bring a new paradigm compared to conventional numerical methods such as Finite Volume or Finite Element Methods. The core of the method consists in enforcing the physical model thanks to the loss function by minimizing the residual of the operators. Although these methods show promising results on academic problems, they also bring many specific questions regarding their benefits for complex applications and their mathematical properties. The present Ph.D proposal aims at studying both aspects.
The candidate will first perform a state of the art study in order to understand the PINNs approach and their potential as a industrial grade simulation method. We propose then to focus on several problems involving different types of complexity issued from physical processes applications like the two-phase flows or the coupling of neutronics and thermal-hydraulics.
University / doctoral school
Ecole Doctorale de Mathématiques Hadamard (EDMH)
Paris-Saclay
Thesis topic location
Site
Saclay
Requester
Position start date
01/10/2024
Person to be contacted by the applicant
DANIEL Geoffrey geoffrey.daniel@cea.fr
CEA
DES/ISAS/DM2S/SGLS/LIAD
Centre d’Etudes de Saclay
DM2S/SGLS, Bât 451
91191 Gif sur Yvette
01 69 08 57 49
Tutor / Responsible thesis director
KOKH Samuel samuel.kokh@cea.fr
CEA
DES/DM2S/SGLS/LCAN
Centre d’Etudes de Saclay
DM2S/SGLS, Bât 451
91191 Gif sur Yvette
0169085456
En savoir plus
https://skokh.pages.math.cnrs.fr/homepage/