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

Статьи, принятые к публикации

ACCRUE: Accurate and Reliable Uncertainty Estimate in Deterministic models Enrico Camporeale, Algo Care'

Enrico Camporeale
University of Colorado

Algo Care'
University of Brescia

DOI: 10.1615/Int.J.UncertaintyQuantification.2021034623

Local Voronoi tessellations for robust multi-wave calibration of computer models Wenzhe Xu, Daniel Williamson, Peter Challenor

Wenzhe Xu
University of Exeter

Daniel Williamson
University of Exeter

Peter Challenor
University of Exeter

DOI: 10.1615/Int.J.UncertaintyQuantification.2021034779

Explicit Estimation of Derivatives from Data and Differential Equations by Gaussian Process Regression Hongqiao Wang, Xiang Zhou

Hongqiao Wang
Central South University

Xiang Zhou
City University of Hong Kong

DOI: 10.1615/Int.J.UncertaintyQuantification.2021034382

Robust Importance Sampling for Bayesian Model Calibration with Spatio-Temporal Data Kyle Neal, Benjamin Schroeder, Joshua Mullins, Abhinav Subramanian, Sankaran Mahadevan

Kyle Neal
Vanderbilt University

Benjamin Schroeder
Sandia National Laboratories

Joshua Mullins
Sandia National Laboratories

Abhinav Subramanian
Vanderbilt University

Sankaran Mahadevan
Vanderbilt University

DOI: 10.1615/Int.J.UncertaintyQuantification.2021033499

Dealing with inconsistent measurements in inverse problems: set-based approach Krushna shinde, Pierre Feissel, Sebastien Destercke

Krushna shinde
université de technologie de compiegne

Pierre Feissel
Université de Technologie de Compiègne

Sebastien Destercke
Université de Technologie de Compiègne

DOI: 10.1615/Int.J.UncertaintyQuantification.2021035536

ERROR CONTROL IN THE NUMERICAL POSTERIOR DISTRIBUTION IN THE BAYESIAN UQ ANALYSIS OF A SEMILINEAR EVOLUTION PDE Maria L. Daza-Torres, J. Cricelio Montesinos-López, Marcos A. Capistrán, J. Andrés Christen, Heikki Haario

Maria L. Daza-Torres
Universidad de Guadalajara

J. Cricelio Montesinos-López
Centro de Investigación en Matemáticas, A.C

Marcos A. Capistrán
Centro de Investigación en Matemáticas, A.C

J. Andrés Christen
Centro de Investigación en Matemáticas, A.C

Heikki Haario
Lappeenranta University of Technology, Department of computational and process engineering and Finnish Meteorological Institute

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033516

Application of the ?-Neumann-Monte Carlo methodology in the quantification of uncertainties in the problem of stochastic bending of Kirchhoff plates Cláudio Roberto Ávila Junior, Roberto Squarcio, João Lucas Cavichiolo, João Morais Neto

Cláudio Roberto Ávila Junior
PPGEM - Federal University of Technology – Paraná

Roberto Squarcio
Federal University of Technology - Paraná, PPGEM

João Lucas Cavichiolo
PPGEM, Federal University of Technology – Paraná

João Morais Neto
Mechanical Engineering Department, Federal University of Paraná - Paraná

DOI: 10.1615/Int.J.UncertaintyQuantification.2020034192

An efficient computational method for parameter identification in the context of random set theory via Bayesian inversion Truong Vinh Hoang, Hermann G. Matthies

Truong Vinh Hoang
Chair of Mathematics for Uncertainty Quantification, RWTH-Aachen University

Hermann G. Matthies
Technische Universität Braunschweig

DOI: 10.1615/Int.J.UncertaintyQuantification.2020031869

Isogeometric Methods for Karhunen-Loeve Representation of Random Fields on Arbitrary Multi-patch Domains Ramin Jahanbin, Sharif Rahman

Ramin Jahanbin
The University of Iowa

Sharif Rahman
The University of Iowa

DOI: 10.1615/Int.J.UncertaintyQuantification.2020035185

Multi-Index Sequential Monte Carlo Methods for partially observed Stochastic Partial Differential Equations Kody Law, Ajay Jasra, Yaxian Xu

Kody Law
University of Manchester

Ajay Jasra
KAUST

Yaxian Xu
NUS

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033219

Method of Distributions for Systems with Stochastic Forcing Rik Rutjens, Gustaaf Jacobs, Daniel Tartakovsky

Rik Rutjens
San Diego State University

Gustaaf Jacobs
San Diego State University

Daniel Tartakovsky
Stanford University

DOI: 10.1615/Int.J.UncertaintyQuantification.2020031940

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