The reliability sensitivity index based on the safety/failure classification of model predictions
is a valuable tool to measure how uncertain parameters affect the failure of engineering …

The mathematical and computational models in engineering applications commonly have
multiple outputs, so it is critical to develop global sensitivity analysis (GSA) for multivariate …

Feature attribution methods (AMs) are a simple means to provide explanations for the
predictions of black-box models like neural networks. Due to their conceptual differences …

Global sensitivity analysis has mainly been analyzed for scalar output and static models,
though many mathematical and computational models used in engineering produce …

This paper presents a novel reliability-oriented global sensitivity analysis method using the
Subset Simulation (SS) method and the Space Partition (SP) scheme. It can exploit the …

Simulation models have been widely used for thermochemical energy storage to better
understand its behavior and consequently to improve operational control of the device …

Ensemble-based uncertainty quantification and global sensitivity analysis of environmental
models requires generating large ensembles of parameter-sets. This can already be difficult …

In risk analysis, sensitivity measures quantify the extent to which the probability distribution
of a model output is affected by changes (stresses) in individual random input factors. For …

The generalized distance component (GDISCO) approach, which uses the property that the
energy distance is rotationally invariant in high-dimensional space to measure the …

Dengue disease is a major problem for public health surveillance entities in tropical and
subtropical regions having a significant impact not only epidemiological but social and …