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Experimental Mechanics

Physics-Informed Neural Network Based Digital Image Correlation Method

  • 07-01-2025
  • Research paper
Published in:
Authors
B. Li
S. Zhou
Q. Ma
S. Ma
Published in
Experimental Mechanics | Issue 2/2025

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Abstract

The article presents a groundbreaking Physics-Informed Neural Network (PINN) approach for Digital Image Correlation (DIC), addressing the limitations of traditional subset-based methods. By incorporating the photometric consistency assumption into the neural network framework, PINN-DIC directly solves the displacement field, offering superior accuracy and robustness in non-uniform deformation fields. The method is validated through extensive simulations and real experiments, showcasing its effectiveness in complex deformation measurements. The paper also highlights the advantages of PINN-DIC over other deep learning-based DIC methods, such as unsupervised learning DIC, and its potential for future research in integrating inverse problem-solving frameworks.
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previous article Comparison of Hall–Petch Law with an Elastic Limit Identification Method Using Kinematic Field Measurements
next article Investigating De-Bonding Using an Impact Loaded Blister Test
Title
Physics-Informed Neural Network Based Digital Image Correlation Method
Authors
B. Li
S. Zhou
Q. Ma
S. Ma
Publication date
07-01-2025
Publisher
Springer US
Published in
Experimental Mechanics / Issue 2/2025
Print ISSN: 0014-4851
Electronic ISSN: 1741-2765
DOI
https://doi.org/10.1007/s11340-024-01139-w
This content is only visible if you are logged in and have the appropriate permissions.

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