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Parameteridentifikation (MB-48)

Parameter identification (MB-48)


The course “Parameter Identification” will start on April 6th as a Moodle online class.

Even if the the semester start will be postponed due to the outbreak of the corona virus, we aim at starting the course on April 6th. We will provide you the course material in the form of digital content and modern methods, e.g., digital lecture notes, screen casts, an online forum for discussion, etc.

Note that these announcements are made based upon the current situation, and that these arrangement may be subject to change based on new developments. At the moment, for instance, we do not know if and how examinations will take place. Please check this website and the Moodle class for updates.

The entire class will be organized and provided on Moodle by the name “Parameteridentifikation SS2020”. Joining the Moodle class is obligatory. You will receive the password by sending an e-mail to serhat.ayguen@tu-dortmund.de from your TU-Dortmund account until April 24th, using the subject: Anmeldung Moodle Parameteridentifikation.


The identification of model parameters is of utmost importance for accurate numerical simulations of complex materials and structures. Usually, the model parameters are computed by minimizing the distance between experimental data and their numerical counterparts, e.g., by employing a least-squares method.

In order to solve the aforementioned minimization problems, the fundamentals of non-linear optimization are introduced in the first part of the course. Both theoretical as well as numerical aspects are covered. In addition to gradient-based approaches, generic algorithms are also presented. While unconstrained problems are considered first, the extensions necessary for constrained optimization are subsequently discussed. In addition to the fundamentals, the application of the algorithms to solids mechanics is also addressed, e.g., the parameter identification of elasto-plastic solids. This application includes the parameter identification based on (initial) boundary value problems which are characterized by inhomogeneous fields (such as the strains). 

After successfully participating in the course, the students are able to apply methods of parameter identification to different classes of materials and to implement the respective algorithms in computer codes. These algorithms – as well as their underlying fundamentals – can be transferred to a broad variety of other technical and scientific problems.



Semester Lecturer Dates Location
SS 2019 Prof. Dr.-Ing. Jörn Mosler

Mondays, 08:30-10:00

Wednesdays, 08:30-10:00

MB I, Room 165/
MB I, Room 163 (CIP-Pool)

see the Moodle class online until further notice

Documents Contents
Chapter 0 Motivation
Chapter 1 Preliminaries
Chapter 2.1 Unconstrained non-linear optimization - step size strategies
Chapter 2.2 Unconstrained non-linear optimization - descent directions
Chapter 3 Constrained non-linear optimization
Chapter 4 Uniqueness of model parameters
Chapter 5 Parameter identification (for mechanical problems)


The final exam consists of two parts.

  1. All students will be assigned a programming task in the context of parameter identification. This project and the corresponding results have to be presented in a short presentation. 
  2. The participants have to take an oral exam.