Physics-Informed Learning for Acoustic Inverse Problems: Field Reconstruction, Detection, and Detectabil

Thesis topic details

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-DRT-26-0650  

Direction

DRT

Thesis topic details

Category

Technological challenges

Thesis topics

Physics-Informed Learning for Acoustic Inverse Problems: Field Reconstruction, Detection, and Detectability Analysis in Complex Environments

Contract

Thèse

Job description

This PhD project aims to develop a mathematical and algorithmic framework for solving acoustic inverse problems in complex environments, based on physics-informed learning. By explicitly incorporating the wave equation into artificial intelligence architectures, the objective is to improve acoustic field reconstruction from partial measurements, the localization of mobile sources, and the quantitative analysis of their detectability. The project combines partial differential equation modeling, constrained optimization, and hybrid deep learning. Applications include distributed acoustic sensing systems and the detection of mobile platforms.

University / doctoral school

Ecole Doctorale Sciences des Métiers de l’Ingénieur (SMI )
Arts et Métiers ParisTech (ENSAM)

Thesis topic location

Site

Saclay

Requester

Position start date

01/09/2026

Person to be contacted by the applicant

BOLZMACHER Christian christian.bolzmacher@cea.fr
CEA
DRT/DIASI//LISA
CEA Saclay Nano-INNOV
Institut CARNOT CEA LIST
DIASI/Laboratoire d’Interfaces Sensorielles et Ambiantes
Point courrier 173
91191 Gif sur Yvette CEDEX

01 69 08 02 32

Tutor / Responsible thesis director

BOLZMACHER Christian christian.bolzmacher@cea.fr
CEA
DRT/DIASI//LISA
CEA Saclay Nano-INNOV
Institut CARNOT CEA LIST
DIASI/Laboratoire d’Interfaces Sensorielles et Ambiantes
Point courrier 173
91191 Gif sur Yvette CEDEX

01 69 08 02 32

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