Intrusive and non-intrusive model reduction
Summary
Intrusive and non-intrusive model reduction for parametrized partial differential equations and dynamical systems
Content
- proper orthogonal decomposition
- reduced basis techniques
- offline-online decompositions
- error estimation
- empirical interpolation
- operator learning
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
In the programs
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Intrusive and non-intrusive model reduction
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Intrusive and non-intrusive model reduction
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Intrusive and non-intrusive model reduction
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Intrusive and non-intrusive model reduction
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional