Predicting stress, strain and deformation fields in materials and structures with graph neural networks.

Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge.

Dynamic Measurements Using FDM 3D-Printed Embedded Strain Sensors.

3D-printing technology is opening up new possibilities for the co-printing of sensory elements. While quasi-static research has shown promise, the dynamic performance has yet to be researched. This study researched smart 3D structures with embedded and printed sensory elements.