This lecture series provides an overview of current research in Digital Twins, delivered by members of the SYDSEN research group working on active research topics in the field. Each lecture focuses on a specific topic and covers state-of-the-art methods as well as practical applications. Topics include:

1. Comprehensive Digital Twins
2. Digital Twins for Labor-intensive Manufacturing Systems
3. Integration of Expert Knowledge into Data-Driven Digital Twin Models
4. Human-in-the-Loop Digital Twins
5. Validation of Digital Twins for Production Systems
6. Explainability in Digital Twins
7. Automation of Simulation-Based Decision Support in Data-Driven Digital Twins
8. Digital Twins for Enhanced Reliability in Energy Systems
9. Data-driven Agent-Based Simulation for Digital Twins

By the end of the course, students will understand how Digital Twins are applied in different domains. They will be able to analyze research approaches, identify limitations, and assess the practical relevance of current work. Students will also be able to recognize emerging research directions and innovation opportunities.

Prerequisites

Basic programming experience and foundational knowledge in mathematics, modeling, and data-driven methods.

Learning Objectives

• Conduct critical analysis of Digital Twin research and its practical relevance.
• Evaluate limitations and opportunities for innovation in existing Digital Twin approaches.
• Identify emerging trends across application domains and research areas.
• Connect theoretical knowledge with hands-on experience through demonstrations and tool-based exercises.

Form of Instruction

Lectures covering theoretical foundations and research contributions, complemented by practical sessions including demonstrations, case studies, and tool-based exercises. A detailed course plan will be provided before the start of the semester.

Competence Certificate

The examination will be offered as a written exam (60 minutes).