Physics-Informed Machine Learning for Acoustic Holography // Holographie acoustique basée sur l’apprentissage informé par la physique

Topic description

PhD offer (starting date: Fall )

Context and Objectives: Near-field acoustic holography is an imaging technique based on the measurement of the acoustic field using a microphone array. It enables the reconstruction of acoustic quantities (pressure, particle velocity, intensity) in the vicinity of sound sources, providing a precise spatial and frequency-domain representation of acoustic radiation. Introduced by Williams [1], acoustic holography has gained widespread adoption due to its many advantages: no assumptions on the nature of the sources, sub-half-wavelength spatial resolution, access to multiple field components, and adaptability to both stationary and non-stationary configurations [2]. Reconstructing the acoustic field from near-field measurements constitutes an inverse problem, which has been extensively studied using various regularization strategies, including Tikhonov regularization [3], Bayesian approaches [4], and compress...