The lectures will cover the following:
• Design of experiment
• Classical parametrized model classes: neural networks, polynomial chaos, gaussian process, support vector machine
• Learning methods
• Validation metrics and techniques for error estimation
Tutorial and homework sessions will allow the students to practice and construct metamodels on benchmarks or data bases. The students will also work on a mini-project that will use metamodeling for risk and reliability analysis.