C
Costas Papadimitriou
Researcher at University of Thessaly
Publications - 249
Citations - 6799
Costas Papadimitriou is an academic researcher from University of Thessaly. The author has contributed to research in topics: Bayesian inference & Uncertainty quantification. The author has an hindex of 37, co-authored 220 publications receiving 5417 citations. Previous affiliations of Costas Papadimitriou include Hong Kong University of Science and Technology & California Institute of Technology.
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Probabilistic damage identification of a designed 9-story building using modal data in the presence of modeling errors
TL;DR: This study proposes a process to mitigate the effects of modeling errors by selecting the optimal subset of modes and the optimal modal residual weights and shows that the structural damages can be identified with negligible bias when the proposed likelihood and updating process is implemented.
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Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
Wang-Ji Yan,Wang-Ji Yan,Dimitrios Chronopoulos,Costas Papadimitriou,Sergio Cantero-Chinchilla,Guo Shu Zhu +5 more
TL;DR: A cheap and fast Kriging surrogate model built and based on a set of training points generated with an experiment design strategy in tandem with a hybrid Wave and Finite Element (WFE) computational scheme is proposed in this study to overcome the computational challenges of repeated likelihood evaluations.
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Accounting for amplitude of excitation in model updating through a hierarchical Bayesian approach: Application to a two-story reinforced concrete building
TL;DR: A hierarchical Bayesian model updating approach is proposed for model calibration and response prediction of dynamic structural systems in a wide range of excite levels where the linear equivalent stiffness of different structural components are updated as functions of excitation amplitude.
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New Approximations for Reliability Integrals
TL;DR: In this article, a new asymptotic expansion is applied to approximate reliability integrals, which reduces the problem of evaluating a multidimensional probability integral to solving an unconstrained minimization problem.
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Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions
TL;DR: A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics.