[THIS COURSE WILL FIRST BE OFFERED IN THE 2025 WINTER TERM]

Digital twins are real-time virtual replicas of physical assets, typically used for real-time monitoring and control of complex systems, e.g., for energy-efficiency and resource optimization.  This course provides an introduction to the foundational engineering areas required to develop digital twins: modeling and simulation for real-time reasoning, collecting and managing data to connect the digital twin to the physical system, and controlling the physical system through closed-loop control.
You will be exposed to the technical depths of these engineering areas and learn about their integration in specific digital twin architectures and lifecycle models. In addition to the lectures, you will process related scientific literature (from domains such as smart cities, industrial automation, or precision agriculture) to further customize your learning experience.

Format

– Lectures (~70%)
– Guest lectures from international and industry experts (~10%)
– Student seminars, paper summaries and presentations (~20%)

Evaluation

Individual literature processing and presentation: 50%
Students process scientific literature over the course of the term, about 2-3 papers in a specific digital twin topic (e.g., applications in smart cities, simulation foundations, AI for digital twins). They prepare a report on the topic by synthesizing knowledge and drawing conclusions. (About 3-4 pages.) They present their report in the final weeks for the class. Papers to be approved by the instructor.

Exam: 50%

Topics covered

Broader context and architecture of digital twins (10% of lecture time)
– Digital transformation, Industry 4.0 and 5.0, Society 5.0
– Digital twin technology and the twinning paradigm.
– Reference models, architectural models.

Reasoning by modeling and simulation (40%)
– Modeling heterogeneous systems for digital twins
– Simulation of heterogeneous systems for digital twins: discrete, continuous, and hybrid (co-)simulation. Real-time simulation. DEVS, Modelica, FMI/FMU.

Connecting digital and physical twins (40%)
– Collecting data. Internet of Things. Event stream processing, complex event processing.
– Connecting and managing data. From data to information: connecting data to models. Models at run-time. Inferred data, state estimation, Kalman filters and particle filters.
– Controlling physical twins. Foundations of control theory, closed-loop control, negative feedback, PID controllers.

Implementation and applications (10%)
– Deployment and operation: commercial and open-source solutions
– Sustainability and digital twins: digital twins for sustainable systems and sustainable engineering of digital twins