Model Order Reduction in Computational Solid Mechanics
General Information about the Course


TUCaN module number: TBD
Cycle: Every winter semester
Lecturer: Prof. Dr.-Ing. Dominik Schillinger
Language: English

All information regarding the lecture and the exercise of the current semester can be found in the respective courses in moodle and TUCaN.

TUCaN course number of the lecture and exercise: TBD


  • Part I: Fundamentals and mathematical background
    • Motivation of reduced order modeling (many-query, real-time, high-dimensional scenarios)
    • Traditional engineering approaches: static condensation, modal decomposition
    • Foundations of parametrized partial differential equations
    • Proper orthogonal decomposition, snapshots, offline/online strategies
    • Reduced basis methods, Galerkin projection and orthonormalization, sampling strategies
  • Part II: Model order reduction in computational solid mechanics
    • Computational homogenization of heterogeneous materials
    • Generalized multiscale finite element methods
  • Part III: Advanced topics
    • Stability, system conditioning, empirical interpolation methods

Qualification goals

Model order reduction techniques decrease the complexity of mathematical models in numerical simulations. They play a key role in dealing with parametrized systems that require fast and frequent model evaluation. This course provides an introduction to model order reduction with a focus on applications in computational solid mechanics. After successful completion of the first part, students know the foundations of parametrized partial differential equations and understand the challenges associated with their finite element approximation. They know the mathematical basis of different reduced order methods, including their specific advantages, and are able to decide in what scenario which method should be applied. After completion of the second part, students are able to bridge the gap between multiscale models in solid mechanics, discretization based on the finite element method, and model order reduction. They are able to implement different model order reduction techniques for linear problems and are able to critically assess their results in terms of accuracy and efficiency. After completing the third part, students understand limitations of model order reduction techniques and know about open questions and challenges related to current research.


Solid knowledge on the finite element method and in continuum mechanics are recommended.



There will be an oral exam.

Time and place of the individual exams will be be announced at the appropriate time.

Please bring your student ID and an additional valid photo ID to the exam.

Also take notice of the general information about exams .