Brief Introduction

This Special Session focuses on physics-informed artificial intelligence for health monitoring, reliability assessment, and uncertainty quantification of complex engineering systems. It welcomes advances that integrate physical knowledge, sensing data, and machine learning for fault diagnosis, degradation modeling, prognostics, and reliability analysis. Particular interests include trustworthy and transferable AI, uncertainty quantification, multimodal monitoring, and digital twin-enabled decision support, with applications in structures, manufacturing systems, energy systems, transportation, aerospace, robotics, and other safety-critical engineering systems.