Damage Quantification

By seamlessly integrating a reduced-order digital twin containing damage states and influencing parameters as a decoder within the variational neural network，it enables the individualized, real-time structural damage quantification and parameter calibration across an entire fleet, while accounting for uncertainties.

A state-of-the-art online damage quantification framework based on domain adaptation is presented, and comprehensively demonstrated with a damaged helicopter panel.

A novel domain adaptation method is proposed to assist diagnostic tasks based on labeled data from similar structures or simulations and is then applied to cracked lap shear specimens to assist debonding quantification.