Stiffness Identification

Stiffness identification focuses on accurately determining the resistance of materials or structures to deformation, crucial for diverse applications from structural health monitoring to medical diagnostics and robotics. Current research employs various approaches, including physics-informed neural networks (like DeepONets) and Bayesian hierarchical models, often incorporating multitask learning and self-supervised techniques like contrastive learning to improve accuracy and efficiency. These advancements enable more robust predictions of structural responses, improved anomaly detection in complex systems, and refined control in robotic manipulation, ultimately enhancing safety and performance across numerous fields.