AI and Digital twin for Engineering

Introduce PhD students from various engineering fields to foundational concepts and practical applications of AI and digital twins, highlighting their integration across diverse engineering problems. The course will include

• Overview of AI techniques and digital twins applied to engineering disciplines, with emphasis on the sustainability.
• Data management and information flow.
• Real-world case studies (e.g., predictive maintenance, process optimization).
• Exploring next steps in AI and digital twin advancements for engineering.

By the end of the course, students will be able to:

• Identify opportunities for digital twins and AI in their field.
• Design basic digital twin or AI models for their own PhD topic.
• Recognize and address ethical and data-related challenges.

Lectures, colloquia, self studies, preparation of an individual project report. The topic of the report will be related to the research areas covered by the course, and it may be related to the actual PhD project of each student. When there is less than 6 students, the course will designed for mainly self study.

The course can be given online by request.

To have access to exam, students should join 70% of lectures.