Introduction on basic statistics, probability theory and uncertainty modeling in the context of engineering decision making. Emphasis is given to the aspects of probabilistic model building, hypothesis testing and model verification. Basic tools are introduced for assessing probabilities as needed in risk analysis. Finally the concepts of decision theory are provided.

This course introduces the fundamental concepts of the Finite Element Method (FEM), covering element formulations, numerical procedures, and modelling aspects. A key focus is the hands-on implementation of FEM algorithms in Python.Disclaimer: The course is not about commercial software, but about core concepts and numerical foundations.

The Method of Finite Elements II is a continuation of Method of Finite Elements I. Here, we explore the theoretical and numerical implementation concepts for the finite element analysis beyond the linear elastic behavior. This course aims to offer students with the skills to perform nonlinear FEM simulations using coding in Python.*This course offers no introduction to commercial software.

The lectures address each year different advanced topics in probabilistic engineering analysis and decision making. During the course the students participate actively in the lectures through literature research, presentations and discussions. In turn each student will give at least one presentation related to the topic during the course. Exercises are developed and solved by the students jointly.

Structural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.