Course - AI and Digital twin for Engineering - VB8000
AI and Digital twin for Engineering
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About the course
Course content
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.
Learning outcome
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.
Learning methods and activities
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 3 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.
Compulsory assignments
- Seminar
Further on evaluation
For a re-take of an examination, all assessments during the course must be re-taken.
To take the exam, shall students join at least 70% of lecture/seminars
Specific conditions
Admission to a programme of study is required:
Engineering (PHIV)
Recommended previous knowledge
PhD candidates within research areas related to Engineering.
Required previous knowledge
PhD candidates within research areas related to Engineering.
Course materials
Textbooks, reports, papers, etc. to be announced at the start of the course.
Subject areas
- Computer and Information Science
- Engineering