Description

Problem: Digital Twins (DTs) are an emerging topic in contemporary research and application landscapes. Each year, the number of studies and projects utilizing DTs continues to grow. However, as with any new technology, there is a lack of standardization and norms despite some initial efforts. Consequently, numerous studies propose new DT implementations, architectures, and enabling technologies. In this master’s thesis, you will explore the field, identifying prominent architectures, technologies, and trends. With this knowledge, you will develop a benchmarking framework to enable quantified and qualified testing of DTs. Quantified testing might include performance metrics such as latency, accuracy, scalability, and resource utilization. For example, measuring the time delay between real-time data and DT response, or assessing the precision of predictions against actual outcomes. Qualified testing might involve usability and functional evaluations, such as integration capability assessments or scenario-based testing to ensure the DT performs well under various conditions.

Goal: The developed framework will help judge the significance and impact of new DT inventions and discoveries.

Required Skills and Knowledge:

• Programming proficiency (preferably in Phyton or Java)
• Basic understanding of Digital Twins
• Basic knowledge of Modeling and Simulation
• Basic knowledge of any simulation software appreciated