Surrogate Modeling and Structural Optimization, Summer 2024

Syllabus

News and Announcements

• Class starting & ending date: July 1st ~ July 22, every weekday
• Class time: 9:00 - 10:50AM, Class zoom link
• Matlab Kriging code: KRG.m (Change extension to m after download)
• Lecture 6-1 is added
• Matlab optimizationi toolbox manual: optim_tb.pdf
• Matlab DIRECT code: DIRECT.m (Change extension to m after download)
• Particle swarm optimization code: PSO.zip

Lecture Videos

• Lecture 1: Introduction to surrogate modeling and polynomial response surface
• Lecture 2: Surrogate accuracy
• Lecture 3: Statistical view of PRS and Design of experiments
• Lecture 4: Optimal and space-filling design of experiments
• Lecture 5: Artificial neural network model
• Lecture 6: Radial basis neural network model
• Lecture 7: Krigging surrogate model
• Lecture 8: Optimization problem formulation
• Lecture 9: Optimality criteria 1
• Lecture 10: Optimality criteria 2 & Linear programming
• Lecture 11: Gradient-based unconstrained optimization
• Lecture 12: Gradient-based constrained optimization
• Lecture 13: Gradient-free deterministic optimization
• Lecture 14: Genetic optimization algorithm
• Lecture 15: Multiobjective optimization, NSGA_II, Particle swarm optimization

Lecture Slides

• Lecture 1: Polynomial response surfaces
• Lecture 2: Design of experiments
• Lecture 3: Neural network modeling
• Lecture 4: Kriging surrogate
• Lecture 5: Optimization formulation
• Lecture 6: Optimality criteria
• Lecture 6-1: Linear programming
• Lecture 7: Numerical optimization
• Lecture 8: Global optimization
• Lecture 9: Multiobjective optimization
• Lecture 10: Efficient Global Optimization

References, Textbook

• Chapter 1: Introduction to surrogate modeling
• Chapter 2: Polynomial response surfaces
• Chapter 3: Design of experiments
• Chapter 4: Optimization definition and formulation
• Chapter 5: Numerical optimization algorithms
• Chapter 6: Global search optimization algorithms

Assignments

Exams