In fields where predictions may have vital consequences, uncertainty quantification (UQ)
plays a crucial role, as it enables more accurate forecasts and mitigates the potential risks …

Extreme events in society and nature, such as pandemic spikes, rogue waves or structural
failures, can have catastrophic consequences. Characterizing extremes is difficult, as they …

We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in
static networks and choose the categorical mixture (CM) as the parametric family to capture …

This paper addresses the challenge of performing importance sampling in high-dimensional
space (several hundred inputs) in order to estimate the failure probability of a physical …

In reliability analysis, high dimensional problems pose challenges to many existing
sampling methods. Cross-entropy based Gaussian mixture importance sampling has …

We identify the zero count problem (or overfitting) of cross-entropy-based methods in the
context of network reliability assessment, and propose a consistent Bayesian estimator that …

We introduce a novel framework called REIN: Reliability Estimation by learning an
Importance sampling (IS) distribution with Normalizing flows (NFs). The NFs learn probability …

The increasing complexity of modern engineering systems has motivated a shift of research
focus from component-level reliability to system reliability with interdependent components …

We introduce a class of acquisition functions for sample selection that lead to faster
convergence in applications related to Bayesian experimental design and uncertainty …

We propose a deep importance sampling method that is suitable for estimating rare event
probabilities in high-dimensional problems. We approximate the optimal importance …