Sparse wavefield reconstruction and defect indication in low-SNR laser ultrasonics using physics-informed neural networks.

Authors

Baoding Wang, Jiuzhang Li, Haodong Chen, Liequan Wu, Pan Ma, Bin Yang, Jun Zhang

Published in

Ultrasonics. Volume 168. Pages 108194. Jun 10, 2026. Epub Jun 10, 2026.

Abstract

Full-optical laser ultrasonics (LOU) provides broadband, non-contact wavefield measurements but is practically limited by low signal-to-noise ratios (SNR) and long scanning times. In these conditions, conventional interpolation and purely data-driven networks often fail to recover physically consistent fields. This study experimentally investigates a Kirchhoff-Love-plate-constrained physics-informed neural network (KL-PINN) framework for sparse wavefield reconstruction and residual-based defect indication under simultaneous low-SNR and sparse-sampling conditions in full-optical LOU. By embedding a Kirchhoff-Love thin-plate equation into the learning process, the KL-PINN imposes an effective flexural-wave physics constraint consistent with the experimentally observed A0-dominated dispersive response. Experiments on aluminum plates demonstrate that the method reconstructs high-fidelity wavefields from only three signal averages (SNR ≈ 6 dB), achieving quality comparable to 300-average full scans with a substantially reduced pulse-count-limited acquisition burden. Compared with the supervised U-Net baseline under the tested random temporal split, the KL-PINN achieved higher reconstruction fidelity from sparse low-SNR inputs without requiring dense high-SNR labels for every frame. Furthermore, the accumulated KL-PDE residual provides a label-free scattering-footprint indicator associated with defect-induced wave interaction. These results indicate that physics-informed learning can reduce the acquisition burden in the tested thin-plate, full-optical LOU configuration. The experimental datasets, together with the KL-PINN implementation, are made publicly available to support further research. https://github.com/wangbaoding0816/Laser_Ultrasound_Reconstruction_PINN.

PMID:
42430858
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

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