Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty von Faes, Matthias, Moens, David

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Engineering analysis with probability boxes: A review on computational methods von Faes, Matthias G.R., Daub, Marco, Marelli, Stefano, Patelli, Edoardo, Beer, Michael

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Fully decoupled reliability-based design optimization of structural systems subject to uncertain loads von Faes, Matthias G.R., Valdebenito, Marcos A.

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Parallel adaptive Bayesian quadrature for rare event estimation von Dang, Chao, Wei, Pengfei, Faes, Matthias G.R., Valdebenito, Marcos A., Beer, Michael

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The sub-interval similarity: A general uncertainty quantification metric for both stochastic and interval model updating von Zhao, Yanlin, Yang, Jianhong, Faes, Matthias G.R., Bi, Sifeng, Wang, Yao

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Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric von Yang, Lechang, Bi, Sifeng, Faes, Matthias G.R., Broggi, Matteo, Beer, Michael

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Operator norm theory as an efficient tool to propagate hybrid uncertainties and calculate imprecise probabilities von Faes, Matthias G.R., Valdebenito, Marcos A., Moens, David, Beer, Michael

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Bounding the first excursion probability of linear structures subjected to imprecise stochastic loading von Faes, Matthias G.R., Valdebenito, Marcos A., Moens, David, Beer, Michael

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Inhomogeneous interval fields based on scaled inverse distance weighting interpolation von van Mierlo, Conradus, Faes, Matthias G.R., Moens, David

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Structural reliability analysis: A Bayesian perspective von Dang, Chao, Valdebenito, Marcos A., Faes, Matthias G.R., Wei, Pengfei, Beer, Michael

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Elucidating appealing features of differentiable auto-correlation functions: A study on the modified exponential kernel von Faes, Matthias G.R., Broggi, Matteo, Spanos, Pol D., Beer, Michael

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Partially Bayesian active learning cubature for structural reliability analysis with extremely small failure probabilities von Dang, Chao, Faes, Matthias G.R., Valdebenito, Marcos A., Wei, Pengfei, Beer, Michael

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Interval uncertainty propagation by a parallel Bayesian global optimization method von Dang, Chao, Wei, Pengfei, Faes, Matthias G.R., Valdebenito, Marcos A., Beer, Michael

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Fully decoupled reliability-based optimization of linear structures subject to Gaussian dynamic loading considering discrete design variables von Faes, Matthias G.R., Valdebenito, Marcos A.

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Efficient imprecise reliability analysis using the Augmented Space Integral von Yuan, Xiukai, Faes, Matthias G.R., Liu, Shaolong, Valdebenito, Marcos A., Beer, Michael

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Efficient decoupling approach for reliability-based optimization based on augmented Line Sampling and combination algorithm von Yuan, Xiukai, Valdebenito, Marcos A., Zhang, Baoqiang, Faes, Matthias G.R., Beer, Michael

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Bayesian probabilistic propagation of hybrid uncertainties: Estimation of response expectation function, its variable importance and bounds von Dang, Chao, Wei, Pengfei, Faes, Matthias G.R., Beer, Michael

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Estimation of an imprecise power spectral density function with optimised bounds from scarce data for epistemic uncertainty quantification von Behrendt, Marco, Faes, Matthias G.R., Valdebenito, Marcos A., Beer, Michael

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Local explicit interval fields for non-stationary uncertainty modelling in finite element models von Callens, Robin R.P., Faes, Matthias G.R., Moens, David

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Interval methods for lack-of-knowledge uncertainty in crash analysis von van Mierlo, Conradus, Burmberger, Lukas, Daub, Marco, Duddeck, Fabian, Faes, Matthias G.R., Moens, David

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