The course equips students with theoretical foundations and practical skills to model, propagate, and
mitigate uncertainties in structural analysis. Students will be able to define an uncertainty quantification
problem, evaluate the effect of aleatory, epistemic as well as polymorphic uncertainty onto computational
models and to interpret the results. It delves into surrogate modeling methods that approximate high-
fidelity simulations, enabling efficient uncertainty assessment in complex systems. Applications to structural
reliability, optimization, and risk-informed decision-making are emphasized, with hands-on experience using
state-of-the-art computational tools.
- Kursleiter/in: Ba Trung Cao
- Kursleiter/in: Gerrit Neu
- Kursleiter/in: Stefanie Maria Schoen
Semester: WT 2025/26