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Sparse wavefield reconstruction based on Physics-Informed neural networks.

Bin Xu1, Yun Zou1, Gaofeng Sha2

  • 1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China.

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Summary
This summary is machine-generated.

This study introduces a new method using physics-informed neural networks to reconstruct full wavefield data from sparse measurements in laser ultrasonic testing. This significantly reduces data acquisition time while maintaining high accuracy.

Keywords:
Laser UltrasonicNon-destructive TestingPhysics-Informed Neural NetworksWavefield Reconstruction

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Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Computational Physics

Background:

  • Laser ultrasonic (LU) devices are crucial for internal material characterization.
  • Acquiring complete wavefield data with LU is time-consuming.
  • Reducing acquisition time is essential for practical LU applications.

Purpose of the Study:

  • To develop a method for reducing data acquisition time in LU testing.
  • To reconstruct complete wavefield data from sparse measurements.
  • To improve the efficiency of internal material information retrieval.

Main Methods:

  • Utilizing sparse sampling of experimental data as input.
  • Employing physics-informed neural networks (PINNs).
  • PINNs learn wave propagation characteristics to reconstruct the full wavefield.

Main Results:

  • Achieved 95% reconstruction accuracy.
  • Successfully reconstructed full wavefield data using only 1/400th of total measurements.
  • Demonstrated the effectiveness of the PINN-based approach.

Conclusions:

  • The proposed method significantly reduces data acquisition time for LU testing.
  • This technique is applicable to various wavefield reconstruction tasks, including online monitoring.
  • Physics-informed neural networks offer an efficient solution for sparse wavefield reconstruction.