In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of
output quantities of complex differential models with random input data, using gradient …

Forward simulation–based uncertainty quantification that studies the distribution of
quantities of interest (QoI) is crucial for computationally robust engineering design and …

Multilevel Monte Carlo (MLMC) method, enhanced by a smoothing technique based on
kernel density estimation (KDE), is coupled with the finite-difference time-domain (FDTD) …

Fiber orientation is decisive for the mechanical properties and thus for the performance of
composite materials. During manufacturing, variations in material and process parameters …

All collected data on any physical, technical or economical process is subject to uncertainty.
By incorporating this uncertainty in the model and propagating it through the system, this …

The uncertainty quantification (UQ) of neutronics calculations is of paramount importance for
the study of spent nuclear fuel (SNF). After its removal from a nuclear power plant, SNF …

This work combines optimization under uncertainty (OUU) with the multilevel Monte Carlo
(MLMC) method. For MLMC, we present new estimators for variance, standard deviation …

This work aims to study the effects of wind uncertainties in civil engineering structural
design. Optimising the design of a structure for safety or operability without factoring in these …