Semester projects
Project proposals Spring Autumn 2026
The following is a list of proposals for semester projects offered at the Chair of Risk, Safety and Uncertainty Quantification. To enquire about conducting a project at the Chair, please directly contact the responsible supervisor.
Optimization of truss structures
Supervisor: M. Moustapha
Optimization is a major task in the design of structures where the analyst seeks to reduce the cost while ensuring that some performance criteria are met.
Many approaches have been developed in the literature depending on whether uncertainties are directly accounted for in the design process or not. The goal of this project is to perform a comparative study of various methods for deterministic and reliability-based design optimization (respectively DDO and RBDO).
In this project, students will design a truss structure using Abaqus as main case study. They will then proceed to optimize this structure in various configurations using UQLab, the Chair’s Matlab platform for uncertainty quantification.
Prerequisites: At least one of the following two courses
Additional information
- Group work: Yes (2)
Gaussian process modeling in high dimensions
Supervisor: M. Moustapha
Gaussian process (GP) models are widely used as surro-gate models for complex engineering systems due to their flexibility and built-in uncertainty quantification. However, their application in high-dimensional settings remains chal-lenging because of the curse of dimensionality and the diffi-culty of identifying relevant input variables. The goal of this project is to investigate strategies for constructing efficient GP models in high dimension by exploiting variable im-portance. In a first step, the student will explore sample-based sensitivity analysis techniques to identify the most influential inputs. Based on this information, custom covari-ance functions will be designed that reflect anisotropy and sparsity in the input space. The project will assess how such tailored kernels improve prediction accuracy and computa-tional efficiency compared to standard isotropic kernels. Im-plementation will be carried out in MATLAB, using UQLab as the primary platform.
Prerequisites
- Uncertainty Quantification in Engineering / Uncertainty Quantification and Surrogate Modeling
- Good MATLAB proficiency
Additional information
- Group work: No
Exploring the hybrid PCE-GPR method
Supervisor: N. Lüthen
Polynomial Chaos expansion (PCE) and Gaussian process modelling, also known as Kriging, are two well-established methods in uncertainty quantification in engineering. Recently, a novel hybrid approach has been proposed, which develops Gaussian process models with specific kernels related to orthonormal polynomials (Manfredi, 2025). The method reportedly outperforms sparse PCE, and is able to provide confidence information on post-processed PCE quantities such as moments and Sobol' indices. The goal of this thesis is to understand and implement this innovative method and explore its advantages and drawbacks.
Prerequisites:
- Uncertainty Quantification in Engineering / Uncertainty Quantification and Surrogate Modeling
- Interest in mathematical topics
- Proficiency in MATLAB programming
Additional information:
- Group work: No
Neural operators for civil engineering
Supervisor: S. Marelli
Neural operators are a recently proposed data-driven technique to emulate the behavior of complex, time-dependent models. While the body of literature on their properties is constantly growing, few applications exist to mechanical and civil engineering. The goal of this project is to benchmark their performance in Engineering scenarios.
This project will assess the usability and performance of the NeuralOperator pytorch package (https://github.com/neuraloperator/neuraloperator) on a multi-story building subject to seismic excitation.
Prerequisites:
- Uncertainty Quantification in Engineering
- Basic Python proficiency
Additional information:
- Group work: No
Completed projects
For a list of past semester projects conducted at our Chair, click here.