Please login first
Bruno Sudret  - - - 
Top co-authors See all
A. Younes

244 shared publications

Department of Pathology and Laboratory Medicine, McGovern Medical School; The University of Texas Health Science Center at Houston; 6431 Fannin Street Houston TX 77030 USA

Irena Hajnsek

167 shared publications

Department of Environmental Engineering, ETH Zurich, Zurich, Switzerland

Fabrice Mortessagne

137 shared publications

Institut de Physique de Nice, Université Côte d’Azur, CNRS, 06100 Nice, France

Odile Picon

119 shared publications

ESIEE-Paris, CNAMESYCOM (EA 2552), UPEMLV, Université Paris-Est, Marne-la-Vallée, France

Eleni N. Chatzi

100 shared publications

Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland

116
Publications
0
Reads
0
Downloads
301
Citations
Publication Record
Distribution of Articles published per year 
(1970 - 2017)
Total number of journals
published in
 
19
 
Publications See all
Article 0 Reads 0 Citations Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators - application to extreme loads on wind ... Imad Abdallah, Christos Lataniotis, Bruno Sudret Published: 01 October 2018
Probabilistic Engineering Mechanics, doi: 10.1016/j.probengmech.2018.10.001
DOI See at publisher website
Article 0 Reads 0 Citations Development of Probabilistic Dam Breach Model Using Bayesian Inference Samuel Jürg Peter, Annunziato Siviglia, Joseph Benjamin Nage... Published: 04 July 2018
Water Resources Research, doi: 10.1029/2017wr021176
DOI See at publisher website
Article 0 Reads 2 Citations Analyzing natural convection in porous enclosure with polynomial chaos expansions: Effect of thermal dispersion, anisotr... Noura Fajraoui, Marwan Fahs, Anis Younes, Bruno Sudret Published: 01 December 2017
International Journal of Heat and Mass Transfer, doi: 10.1016/j.ijheatmasstransfer.2017.07.003
DOI See at publisher website
PREPRINT 0 Reads 0 Citations Uncertainty quantification in urban drainage simulation: fast surrogates for sensitivity analysis and model calibration Joseph B. Nagel, Jörg Rieckermann, Bruno Sudret Published: 11 September 2017
ABS Show/hide abstract
This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sensitivity measures such as the Sobol indices are obtained directly from the expansion coefficients. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.
PREPRINT 0 Reads 0 Citations An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability a... Stefano Marelli, Bruno Sudret Published: 05 September 2017
ABS Show/hide abstract
Polynomial chaos expansions (PCE) have seen widespread use in the context of uncertainty quantification. However, their application to structural reliability problems has been hindered by the limited performance of PCE in the tails of the model response and due to the lack of local metamodel error estimates. We propose a new method to provide local metamodel error estimates based on bootstrap resampling and sparse PCE. An initial experimental design is iteratively updated based on the current estimation of the limit-state surface in an active learning algorithm. The greedy algorithm uses the bootstrap-based local error estimates for the polynomial chaos predictor to identify the best candidate set of points to enrich the experimental design. We demonstrate the effectiveness of this approach on a well-known analytical benchmark representing a series system, on a truss structure and on a complex realistic slope-stability problem.
Article 0 Reads 0 Citations Editorial: Special Issue of ESREL 2015 Luca Podofillini, Bruno Sudret, Božidar Stojadinović, Enrico... Published: 04 August 2017
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, doi: 10.1177/1748006X17724236
DOI See at publisher website
Top