Physics-informed Machine Learning for Acoustic Holography

Physics-Informed Machine Learning for Acoustic Holography // Holographie acoustique basée sur l’apprentissage informé par la physique:
- Réf ABG-134977
- Sujet de Thèse
- 26/01/2026
- Contrat doctoral
- LAUM UMR CNRS 6613
- Lieu de travail
- Le Mans
- Pays de la Loire
- France
- Intitulé du sujet
- Physics-Informed Machine Learning for Acoustic Holography // Holographie acoustique basée sur l’apprentissage informé par la physique
- Champs scientifiques
- Sciences de l’ingénieur
- Mots clés
- acoustics, artificial intelligence, holography, signal processing, acoustique, holographie, intelligence artificielle, traitement du signal Description du sujet: PhD offer (starting date: Fall 2026) 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 compressive sensing or sparse regularization methods [5]. However, with the emergence of Physics-Informed Neural Networks (PINNs), new approaches can now be considered to address the inverse problem of acoustic field reconstruction [6]. Recent studies have begun to explore this research direction [7-12]. For instance, the authors of [7] proposed a method to estimate and reconstruct the sound field of a room from a limited set of experimental impulse responses using a PINN framework. Acoustic wave propagation has also been investigated using PINNs in [8], including scenarios involving obstacles of various shapes and sizes [9]. Furthermore, the Kirchhoff-Helmholtz integral has been employed to incorporate physical constraints into the forward simulation of acoustically reconstructed fields [10, 11]. This principle was extended by Luan et al. [12], who proposed a hybrid approach combining PINNs with sparse field discretization for planar acoustic holography.
- Continuous field reconstruction without restriction to a discrete mesh;
- Reconstruction from potentially irregular measurement configurations;
- Solution of the inverse problem with reduced computational complexity;
- Extrapolation of the reconstructed field beyond the measurement region. In addition to PINNs, recent approaches based on Fourier Neural Operators (FNOs) offer a powerful and highly generalizable alternative [13]. FNOs aim to solve inverse problems by learning operators through collocation in Fourier space. This PhD project is structured around the following objectives: 2. Experimentation and validation on real-world cases:A significant part of the work will be devoted to experimental validation on realistic configurations. Measurements will be conducted using the microphone arrays and data acquisition systems available at the laboratory, complemented by simulations of known acoustic fields (e.g., radiation from a vibrating plate). The performance of PINN-based models will be systematically compared with classical acoustic holography techniques (Tikhonov, compressive, Bayesian) in terms of accuracy, robustness, and computational complexity. 3. Extension to non-stationary acoustic fields:The study will initially focus on stationary fields and will then be extended to time-varying situations. The objective is to develop a time-dependent formulation suitable for non-stationary sources and to investigate the feasibility of real-time acoustic holography. This extension will test the ability of PINNs to represent the dynamic evolution of acoustic fields. 4. Development and evaluation of an FNO-based approach for acoustic field reconstruction:Finally, the thesis will explore the use of FNOs to directly learn the inverse operator mapping near-field measurements to source-level acoustic fields. Performance will be evaluated on both simulated and experimental datasets and compared against PINN-based results. Such a PhD project would contribute to the development of an innovative diagnostic tool for systems whose emitted sounds vary according to their operational state. It would also foster the development of expertise within the laboratory in the field of physics-informed learning, strengthening its scientific positioning in this emerging research area. Profile:**We are looking for a student enrolled in a master’s degree in acoustics o