52 citations · 199 across the 24 of their papers we have counts for
28 papers
A Hierarchical Bayesian Framework for Model-based Prognostics
Xinyu Jia, Iason Papaioannou, Daniel Straub
In prognostics and health management (PHM) of engineered systems, maintenance decisions are ideally informed by predictions of a system's remaining useful life (RUL) based on opera…
Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
Jakob Scheffels, Elizabeth Qian, Iason Papaioannou +1
Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engi…
Sensitivity measures for engineering and environmental decision support
Daniel Straub, Wolfgang Betz, Mara Ruf +4
Information value, a measure for decision sensitivity, can provide essential information in engineering and environmental assessments. It quantifies the potential for improved deci…
A novel stratified sampler with unbalanced refinement for network reliability assessment
Jianpeng Chan, Iason Papaioannou, Daniel Straub
We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network…
Efficient Bayesian inversion for simultaneous estimation of geometry and spatial field using the Karhunen-Loève expansion
Tatsuya Shibata, Michael Conrad Koch, Iason Papaioannou +1
Detection of abrupt spatial changes in physical properties representing unique geometric features such as buried objects, cavities, and fractures is an important problem in geophys…
Enhanced sequential directional importance sampling for structural reliability analysis
Kai Chenga, Iason Papaioannou, Daniel Straub
Sequential directional importance sampling (SDIS) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the produ…
State Space Kriging model for emulating complex nonlinear dynamical systems under stochastic excitation
Kai Chenga, Iason Papaioannoua, MengZe Lyub +1
We present a new surrogate model for emulating the behavior of complex nonlinear dynamical systems with external stochastic excitation. The model represents the system dynamics in…
Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions
Felix Schneider, Iason Papaioannou, Bruno Sudret +1
Bayesian analysis enables combining prior knowledge with measurement data to learn model parameters. Commonly, one resorts to computing the maximum a posteriori (MAP) estimate, whe…
FORM-based global reliability sensitivity analysis of systems with multiple failure modes
Iason Papaioannou, Daniel Straub
Global variance-based reliability sensitivity indices arise from a variance decomposition of the indicator function describing the failure event. The first-order indices reflect th…
Bayesian improved cross entropy method with categorical mixture models
Jianpeng Chan, Iason Papaioannou, Daniel Straub
We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in static networks and choose the categorical mixture as the parametric family to capture the…
Stein Variational Rare Event Simulation
Max Ehre, Iason Papaioannou, Daniel Straub
Rare event simulation and rare event probability estimation are important tasks within the analysis of systems subject to uncertainty and randomness. Simultaneously, accurately est…
Variance-based reliability sensitivity with dependent inputs using failure samples
Max Ehre, Iason Papaioannou, Daniel Straub
Reliability sensitivity analysis is concerned with measuring the influence of a system's uncertain input parameters on its probability of failure. Statistically dependent inputs pr…
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…
Bayesian improved cross entropy method for network reliability assessment
Jianpeng Chan, Iason Papaioannou, Daniel Straub
We propose a modification of the improved cross entropy (iCE) method to enhance its performance for network reliability assessment. The iCE method performs a transition from the no…
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…
Sparse Bayesian Learning for Complex-Valued Rational Approximations
Felix Schneider, Iason Papaioannou, Gerhard Müller
Surrogate models are used to alleviate the computational burden in engineering tasks, which require the repeated evaluation of computationally demanding models of physical systems,…
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…
The ensemble Kalman filter for rare event estimation
Fabian Wagner, Iason Papaioannou, Elisabeth Ullmann
We present a novel sampling-based method for estimating probabilities of rare or failure events. Our approach is founded on the Ensemble Kalman filter (EnKF) for inverse problems.…
Rare event estimation using stochastic spectral embedding
P. -R. Wagner, S. Marelli, I. Papaioannou +2
Estimating the probability of rare failure events is an essential step in the reliability assessment of engineering systems. Computing this failure probability for complex non-line…
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…
Decision-theoretic reliability sensitivity
Daniel Straub, Max Ehre, Iason Papaioannou
We propose and discuss sensitivity metrics for reliability analysis, which are based on the value of information. These metrics are easier to interpret than other existing sensitiv…
Uncertainty quantification of microstructure variability and mechanical behaviour of additively manufactured lattice structures
Nina Korshunova, Iason Papaioannou, Stefan Kollmannsberger +2
Process-induced defects are the leading cause of discrepancies between as-designed and as-manufactured additive manufacturing (AM) product behavior. Especially for metal lattices,…
Error analysis for probabilities of rare events with approximate models
Fabian Wagner, Jonas Latz, Iason Papaioannou +1
The estimation of the probability of rare events is an important task in reliability and risk assessment. We consider failure events that are expressed in terms of a limit-state fu…