Proceedings of the
35th European Safety and Reliability Conference (ESREL2025) and
the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025)
15 – 19 June 2025, Stavanger, Norway
UncertaintyQuantification.jl: Efficient Reliability Analysis Powered by Julia
1Institute for Risk and Reliability, Leibniz University Hannover, Germany.
2Heudiasyc, Université de technologie de Compiègne, France.
3Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK
4International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai, China
ABSTRACT
This work presents the latest features and developments in UncertaintyQuantification.jl, a simulationbased and open-source framework for uncertainty quantification and risk analysis written in Julia. The framework has undergone extensive development since its initial release in August 2020, and now includes a number of numerical algorithms for reliability analysis, sensitivity analysis and surrogate modelling. While this paper presents all currently available features, the main emphasis is on the recent introduction of imprecise probabilities. The evaluation of probability boxes and intervals in any numerical model is a feature we believe sets UncertaintyQuantification.jl apart from other software in the field. Another important element is the ability to interface with HPC job schedulers such as Slurm. This makes the software accessible to simulation codes that require multiple compute nodes, and allows for scalable parallelisation in both the uncertainty algorithm and the simulator. Adequate illustrative numerical examples are presented throughout the paper to highlight the capabilities of the implemented algorithms.
Keywords: Uncertainty quantification, Reliability analysis, Imprecise probabilities, Software.
