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PhD course in Engineering Systems Design Analytics and AI

21 September, 2023 @ 13:00 - 18 October, 2023 @ 17:00

CREDITS 6.0 credits
EXAMINER Petter Krus, Linköping University (LiU)
REGISTRATION Please register here.
TARGET GROUP PhD student interested in model-based system engineering and design analytics and the application of artificial intelligence.
PREREQUISITES Basic background in engineering. Some experience in Python isadvantageous but not required.
AIM The course aims to equip students with analytical tools for evaluating complex systems, focusing on statistical modelling, machine learning, sensitivity analysis, and computational complexity. It should prepare the participants to take on the transformation implied by the rise and democratization of machine learning and large language models and the ability to use these methods in industrial projects.
LEARNING OUTCOMES After the course, the student shall demonstrate skill and ability in:

  • Generate models based on statistical data regarding relations between different system/component characteristics.
  • Produce sensitivity analysis of a system and be able to draw conclusions regarding robustness and the degree of coupling.
  • Estimate system complexity and make analysis of computational complexity.
  •  Be able to generate extensive programming using Generative AI.
  • Design analytics (8 hours)
    • Axiomatic design.
    • Sensitivity analysis.
    • Robustness.
    • Functional correlation.
  • Machine learning and design space generation (8 hours)
    • Introduction to Machine Learning in design.
    • Multiple regression analysis.
    • Principal component analysis (PCA) using Singular value decomposition (SVD).
    • Neural networks.
    • Design space and parametrization.
  • System structures and modelling (4 hours)
    • The use of Large Language Models (LLMs) for analysis and modeling.
    • Requirement and Functional modeling (UML/SysML).
    • Dependency structure matrix.
    • Connectivity graphs.
  • Information entropy in design (4 hours)
    • Shannon’s Information Theory.
    • Information entropy as a measure for system complexity.
    • Design space quantification.
    • Axiomatic Design and Complexity.
    • Information Entropy in Optimization.
ORGANISATION Lectures, projects, and a one-day examination presentation meeting. The project will be a collaborative project using LLMs to generate analysis codes (tentatively) in Python that reflect the content of the course and apply it to an example related to the individual students research projects.
LITERATURE Course Compendium + Scientific papers
EXAMINATION The main examination task is a written project report that can be aligned towards the PhD students’ research.
The grade is passed/not passed.
  • Sept 21. 13-17: Design analytics I, Axiomatic design, Machine learning in design
  • Sept 22. 13-17: Design analytics II. Sensitivity Analysis, Robustness and functional correlation
  • Oct. 2. 13-17: Machine learning and design space generation I: Subsystem modelling. Regression analysis and Principal component analysis
  • Oct. 10. 13-17: Machine learning and design space generation II. Subsystem modelling. Singular Value Decomposition and Neural Networks.
  • Oct. 11. 13-17: System structures and modelling
  • Oct 18 13-17: Information entropy in Design
  • Oct 23 13-17: Project presentation.


21 September, 2023 @ 13:00
18 October, 2023 @ 17:00
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