Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers

TL;DR

This paper introduces a physics-informed neural operator method to estimate frequency-dependent acoustic surface admittance directly from near-field measurements, improving robustness and accuracy over traditional techniques.

Contribution

It develops a deep operator network that embeds acoustic physics into the learning process, enabling physically consistent, noise-robust in situ surface admittance estimation without explicit forward models.

Findings

Accurately reconstructs real and imaginary admittance components across broad frequencies.

Demonstrates robustness to noise and sparse data sampling.

Validates approach with synthetic data for porous sound absorbers.

Abstract

Accurate knowledge of acoustic surface admittance or impedance is essential for reliable wave-based simulations, yet its in situ estimation remains challenging due to noise, model inaccuracies, and restrictive assumptions of conventional methods. This work presents a physics-informed neural operator approach for estimating frequency-dependent surface admittance directly from near-field measurements of sound pressure and particle velocity. A deep operator network is employed to learn the mapping from measurement data, spatial coordinates, and frequency to acoustic field quantities, while simultaneously inferring a globally consistent surface admittance spectrum without requiring an explicit forward model. The governing acoustic relations, including the Helmholtz equation, the linearized momentum equation, and Robin boundary conditions, are embedded into the training process as physics-based regularization, enabling physically consistent and noise-robust predictions while avoiding frequency-wise inversion. The method is validated using synthetically generated data from a simulation model for two planar porous absorbers under semi free-field conditions across a broad frequency range. Results demonstrate accurate reconstruction of both real and imaginary admittance components and reliable prediction of acoustic field quantities. Parameter studies confirm improved robustness to noise and sparse sampling compared to purely data-driven approaches, highlighting the potential of physics-informed neural operators for in situ acoustic material characterization.