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Iason Papaioannou
Researcher at Technische Universität München
Publications - 110
Citations - 2313
Iason Papaioannou is an academic researcher from Technische Universität München. The author has contributed to research in topics: Reliability (statistics) & Bayesian inference. The author has an hindex of 18, co-authored 92 publications receiving 1347 citations. Previous affiliations of Iason Papaioannou include Nanchang University & Analysis Group.
Papers
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MCMC algorithms for Subset Simulation
TL;DR: A novel approach for MCMC sampling in the standard normal space is introduced and it is demonstrated that the proposed algorithm improves the accuracy of Subset Simulation, without the need for additional model evaluations.
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Numerical methods for the discretization of random fields by means of the Karhunen–Loève expansion
TL;DR: The FEM and the FCM are more efficient than the EOLE method in evaluating a realization of the random field and are suitable for problems in which the time spent in the evaluation of random field realizations has a major contribution to the overall runtime – e.g., in finite element reliability analysis.
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Bayesian Updating with Structural Reliability Methods
Daniel Straub,Iason Papaioannou +1 more
TL;DR: An algorithm for the implementation of BUS is proposed, which can be interpreted as an enhancement of the classic rejection sampling algorithm for Bayesian updating, and its efficiency is not dependent on the number of random variables in the model.
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Sequential importance sampling for structural reliability analysis
TL;DR: In this article, a sequential importance sampling (SIS) algorithm is proposed to estimate the probability of failure in structural reliability in the context of structural reliability problems, which is applicable to general problems with small to moderate number of random variables and is especially efficient for tackling high-dimensional problems.
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Reliability updating in geotechnical engineering including spatial variability of soil
Iason Papaioannou,Daniel Straub +1 more
TL;DR: In this paper, a method for Bayesian updating of the reliability is successfully applied in conjunction with a stochastic nonlinear geotechnical finite element model, where uncertainty in the soil material properties is modelled by non-Gaussian random fields.