The opening symposium will explore the transformative potential of digital twins (DTs), highlighting their role as a cornerstone of digitalization, sustainability, and innovation across multiple domains. Through selected case studies, the session will illustrate how DTs enable real-time monitoring, predictive control, and data-driven optimization, reshaping approaches to system design, quality assurance, and operational management.
In the agritech sector, attention will be given to advanced fertigation systems that integrate IoT, artificial intelligence, and dynamic DTs of agricultural environments. By combining distributed sensor networks with AI-powered decision-making, these systems achieve precise, sector-level optimization of irrigation and fertilization, enhancing both sustainability and resource efficiency.
In the industrial and manufacturing context, DTs are increasingly applied to factory planning, production line monitoring, and process optimization. AI-enabled models not only simulate future scenarios but also allow proactive detection of inefficiencies and faults, resulting in improvements in quality, safety, and productivity. In design and prototyping, DTs further support material optimization, virtual testing, and immersive CAD-integrated environments.
A further focus will be on the challenge of uncertainty quantification, essential to ensuring the reliability and traceability of predictive models. Recent advances demonstrate how uncertainty-aware DTs can identify critical influence factors, propagate measurement uncertainty, and support informed decision-making in applications such as predictive maintenance and quality control.
By connecting perspectives from agriculture, manufacturing, and metrology, the symposium will provide a comprehensive overview of the capabilities and challenges of digital twins, laying the groundwork for the discussions in the following sessions.