Courses and Tutorials

Course Outline

First Week: Introduction to engineering design optimization techniques

  • Introduction to modern tools in engineering design and optimization.
  • Optimization problem formulation
  • Classical optimization methods: gradient-based optimization
  • Stochastic optimization methods: Evolutionary algorithms and metaheuristics.
  • Industrial application I: Optimization techniques in real-world scenarios

Second week: Challenges and consideration in real-world optimization

  • Compromise in design process: Multi-objective optimization
  • Challenges in real-world optimization: design under uncertainty
  • Design for additive manufacturing: Topology optimization
  • Multidisciplinary Design Optimization
  • Industrial application II: Artificial Intelligence in engineering design

Third week: Data science and machine / statistical learning for engineering design optimization

  • How can data help design? The data science and data mining approach
  • Surrogate modeling and machine learning: modern approximation tools
  • Introduction to Deep Learning for engineering
  • Future challenges in optimization


Python tutorials will be available after each lectures on the same day. Guided by tutors, full participants will directly engage in optimization activities through Python codes.

The courses will use Zoom as virtual platform and Python tutorials will be taught through Google Colaboratory.

Virtual Background for Participants

All participants are advised to use the following virtual background during class and tutorial sessions.

Institution Partners

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