Design, Modelling and Validation in Digital Twin Technology

This comprehensive learning path provides an introductory exploration of Digital Twins (DT) technology, its diverse applications and the future challenges it poses. The learning commences with a historical overview of Digital Twins’ practical implementations in various domains and the definition of DT autonomy levels according to their intelligence, setting the stage for a deeper understanding.

To facilitate effective comprehension, the learning path offers insights into the background theory and computational foundations that underpin DT implementations. This foundational knowledge equips learners with a common language and understanding along with the key components that constitute them. The learning then charts a roadmap, from physics-based modelling principles towards data-driven models and Machine Learning (ML) in the digital twin paradigm. Finally, learners are introduced to well-known techniques for validating and evaluating data-driven models using real-world data.