The integration of digital twins with artificial intelligence (AI) and advanced computational methods is reshaping the landscape of structural analysis and mechanical design. This minisymposium will explore the convergence of digital twin technology, finite element modelling, and AI-driven approaches to transform the engineering and simulation of complex structural systems. Digital twins are becoming a basis for structures virtualization with multiple capabilities as real-time monitoring, predictive maintenance, and performance assessment/optimization. By coupling the digital models, including finite element (FE) driven models with experimental data through machine learning, it is possible to achieve unprecedented accuracy in structural analysis, from the micro- to macro-scale.

This session accepts communications that cover the latest advancements in the development and application of digital twins for structures across various industries, including aerospace, civil, automotive, and energy sectors. Key topics will include AI techniques, such as deep learning and neural networks, that enhance the prediction and understanding of structural behaviours, along with advanced structural computational methods to simulate and analyse complex stress, strain, and failure mechanisms. Presentations will highlight how these integrated techniques provide more efficient and accurate design, reduce computational costs, and enable real-time structural health monitoring.