E. Patelli

STRiDe - a Self-Tuning Reduced Intelligent Digital environment

Anshu Sharma, E. Tubaldi, Susmita Naskar, E. Patelli, B. Bhowmik

TL;DR: STRiDe provides a robust, interpretable, and data-efficient foundation for hybrid Digital Twins operating under uncertain, nonlinear, and slowly evolving structural conditions and exemplifies how adaptive operator learning can sustain accuracy, interpretability, and resilience – key attributes for next-generation SHM systems in realistic monitoring environments.

Digital Twin (DT) architectures that combine physics-based modelling with machine learning are redefining structural health monitoring (SHM) as a predictive, self-adaptive discipline. A persistent challenge lies in representing nonlinear and evolving dynamics of real-world system…Read more