PINN-based neural network for photoelastic stress recovery

Optical measurement techniques have emerged as a promising alternative to traditional invasive methods for stress recovery. Recent developments in artificial intelligence have enabled researchers to harness Physics-Informed Neural Networks (PINNs) for solving complex physics-based problems, including stress field recovery from photoelastic fringes. This paper explores the potential of PINNs for non-invasive stress analysis, presenting a novel methodology that integrates photoelastic fringes and physical stress distribution constraints within a deep learning framework. Our results demonstrate the model's robustness in handling diverse stress patterns and its ability to accurately recover stress distributions even when trained on significantly reduced datasets. A comparative analysis shows our PINN-based model's performance surpassing or closely matching existing methods like PhotoelastNet, underscoring its competitive advantage in stress recovery.