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5-Year Impact Factor: 3.179

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ISSN Print: 2152-5080
ISSN Online: 2152-5099

Forthcoming Articles

Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications Nora Lüthen, Stefano Marelli, Bruno Sudret

Nora Lüthen
Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5,8093 Zurich, Switzerland

Stefano Marelli
Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5,8093 Zurich, Switzerland

Bruno Sudret
Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5,8093 Zurich, Switzerland

DOI: 10.1615/Int.J.UncertaintyQuantification.2021036153

Closure Law Model Uncertainty Quantification Andreas Strand, Jørn Kjølaas, Trond H. Bergstrøm, Ingelin Steinsland, Leif Rune Hellevik

Andreas Strand
NTNU

Jørn Kjølaas
SINTEF

Trond H. Bergstrøm
SINTEF

Ingelin Steinsland
NTNU

Leif Rune Hellevik
NTNU

DOI: 10.1615/Int.J.UncertaintyQuantification.2021037714

SPARSE TENSOR PRODUCT APPROXIMATION FOR A CLASS OF GENERALIZED METHOD OF MOMENTS ESTIMATORS Alexandros Gilch, Michael Griebel, Jens Oettershagen

Alexandros Gilch
Uni Bonn, INS

Michael Griebel
Uni Bonn

Jens Oettershagen
Uni Bonn, INS

DOI: 10.1615/Int.J.UncertaintyQuantification.2021037549

A FETI-DP based parallel algorithm for solving high dimensional stochastic PDEs using collocation Gopika Ajith, Debraj Ghosh

Gopika Ajith
Graduate Student

Debraj Ghosh
Indian Institute of Science

DOI: 10.1615/Int.J.UncertaintyQuantification.2021038512

Feedback control for random, linear hyperbolic balance laws Stephan Gerster, Markus Bambach, Michael Herty, Muhammad Imran

Stephan Gerster
RWTH Aachen University

Markus Bambach
TH Zürich

Michael Herty
RWTH Aachen University

Muhammad Imran
BTU Cottbus-Senftenberg

DOI: 10.1615/Int.J.UncertaintyQuantification.2021037183

A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes Raphaël Gautier, Piyush Pandita, Sayan Ghosh, Dimitri Mavris

Raphaël Gautier
Georgia Institute of Technology

Piyush Pandita
GE Research

Sayan Ghosh
GE Research

Dimitri Mavris
Georgia Institute of Technology

DOI: 10.1615/Int.J.UncertaintyQuantification.2021035621

A comprehensive comparison of total-order estimators for global sensitivity analysis Arnald Puy, William Becker, Samuele Lo Piano, Andrea Saltelli

Arnald Puy
Princeton University

William Becker
European Commission, Joint Research Centre

Samuele Lo Piano
University of Reading, School of the Built Environment

Andrea Saltelli
Open Evidence Research, Universitat Oberta de Catalunya

DOI: 10.1615/Int.J.UncertaintyQuantification.2021038133

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