This direction contributes to virtual material testing by integrating physical laws and artificial intelligence (AI) models.

Inputs were designed based on the physics-informed knowledge of the accommodation capability of the macroscopic plastic deformation.

Compared to CPFEM simulations, the advantage of the pseudo models is related to their efficiency.

This direction contributes to structural health monitoring by integrating vibrational data mining and deep learning.

Knowledge-enable feature map generations and tailored deep learning topologies are developed for fast and high accuracy.

State-of-the-art data-driven methods are necessary for real-time monitoring systems. 

This direction contributes to structural health monitoring based on vibrational responses.

Advanced mechanical models and methodologies allow damage diagnosis via analytical and data-driven approaches.