Real-time Digital Twins

This course introduces participants to a range of algorithmic approaches for realizing real-time digital twins, spanning classical simulation techniques as well as model order reduction technologies. All methods are presented through concrete industrial examples to ensure immediate relevance and applicability. Core topics include modal truncation, Krylov methods, Proper Orthogonal Decomposition, and Operator Inference. These will be illustrated through hands-on implementation examples centered around a thermal management use case. The course further explores extensions toward more advanced topics, including active learning, equipping participants with a forward-looking perspective on the field.

Upon completing the course, participants will be able to make well-informed decisions in selecting the most appropriate model order reduction algorithm for their own applications, and to realize corresponding implementations using the provided code building blocks (in the Julia Language). They will also gain valuable insight into more advanced methods, enabling them to tackle increasingly challenging problems with confidence.