mlsanityData to 14 Oct 2026

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

arXiv 2604.07412 ↗First posted 8 Apr 2026cs.LG physics.data-ancited by 0

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Abstract (arXiv)

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.

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References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  3. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  4. 2021Learning the solution operator of parametric partial differential equations with physics-informed DeepOnetscited by 540
  5. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 527
  6. 2021Neural Operator: Learning Maps Between Function Spacescited by 414
  7. 2023Neural Operators for Accelerating Scientific Simulations and Designcited by 221
  8. 2022Applications of physics informed neural operatorscited by 33
  9. 2023Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operatorscited by 12
  10. 2024Room impulse response reconstruction with physics-informed deep learningcited by 10
  11. 2021Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundariescited by 7
  12. 2024Physics and geometry informed neural operator network with application to acoustic scatteringcited by 3
3 more
  1. 2025A data-driven two-microphone method for in-situ sound absorption measurementscited by 2
  2. 2025In situ estimation of the acoustic surface impedance using simulation-based inferencecited by 2
  3. 2026Surface impedance inference via neural fields and sparse acoustic data obtained by a compact arraycited by 1

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