Intrusive and non-intrusive model reduction

Summary

Intrusive and non-intrusive model reduction for parametrized partial differential equations and dynamical systems

Content

Learning Prerequisites

Required courses

Math-250: Advanced Numerical Analysis I

Recommended courses

Math-351: Advanced Numerical Analysis II

Important concepts to start the course

Students should be comfortable with Python programming.

Learning Outcomes

By the end of the course, the student must be able to:

• Apply intrusive and non-intrusive techniques to computational problems
• Explain the main concepts and methods of model reduction
• Assess / Evaluate and compare reduced-order models in terms of accuracy and efficiency

Teaching methods

- Lectures
- Exercises

Expected student activities

Attending lectures
Implementing mathematical methods in a programming language
Finishing exercises

Assessment methods

Form of examination: 40% homeworks and projects and 60% final exam

Resources

Moodle Link

• https://go.epfl.ch/MATH-537