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.