Prof. Dr. Dirk Hartmann, Siemens/TU Darmstadt

The digital twin paradigm has emerged as transformative tool for any industry. Digital twins offer significant opportunities for real-time monitoring, optimization, and decision-making. However, the scalability of digital twins remains limited by current technologies. This poses significant challenges to their broader adoption. Mathematics plays a crucial role in addressing these limitations, providing the foundation for scalable and efficient digital twin implementations.

This talk explores the intersection of applied mathematics and digital twin technologies. We highlight key challenges of digital twins today in detail and discuss opportunities for corresponding mathematical research. Specifically, we will address along selected industrial case studies how Reduced Order Modelling and Scientific Machine Learning reduce manual efforts for real-time model building as well as allow to address new classes of applications.