52 citations · 199 across the 24 of their papers we have counts for
7 papers · 1 filter
Consensus-based rare event estimation
Konstantin Althaus, Iason Papaioannou, Elisabeth Ullmann
In this paper, we introduce a new algorithm for rare event estimation based on adaptive importance sampling. We consider a smoothed version of the optimal importance sampling densi…
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method
Max Ehre, Rafael Flock, Martin Fußeder +2
In inverse problems, the parameters of a model are estimated based on observations of the model response. The Bayesian approach is powerful for solving such problems; one formulate…
On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
Antonios Kamariotis, Luca Sardi, Iason Papaioannou +2
Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on time…
Rare event estimation with sequential directional importance sampling (SDIS)
Kai Cheng, Iason Papaioannou, Zhenzhou Lu +2
In this paper, we propose a sequential directional importance sampling (SDIS) method for rare event estimation. SDIS expresses a small failure probability in terms of a sequence of…
Sequential active learning of low-dimensional model representations for reliability analysis
Max Ehre, Iason Papaioannou, Bruno Sudret +1
To date, the analysis of high-dimensional, computationally expensive engineering models remains a difficult challenge in risk and reliability engineering. We use a combination of d…
Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
Felipe Uribe, Iason Papaioannou, Youssef M. Marzouk +1
The estimation of rare event or failure probabilities in high dimensions is of interest in many areas of science and technology. We consider problems where the rare event is expres…
Multilevel Sequential Monte Carlo for Bayesian Inverse Problems
Jonas Latz, Iason Papaioannou, Elisabeth Ullmann
The identification of parameters in mathematical models using noisy observations is a common task in uncertainty quantification. We employ the framework of Bayesian inversion: we c…