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Fator do impacto: 3.259
FI de cinco anos: 2.547

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ISSN Imprimir: 2152-5080
ISSN On-line: 2152-5099

Próximos artigos

Embedded Model Error Representation for Bayesian Model Calibration Khachik Sargsyan, Xun Huan, Habib N. Najm

Khachik Sargsyan
Sandia National Laboratories

Xun Huan
University of Michigan, Ann Arbor

Habib N. Najm
Sandia National Laboratories

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027384

A weight-bounded importance sampling method for variance reduction Tengchao Yu, Jinglai Li, Linjun Lu

Tengchao Yu
Shanghai Jiaotong Unviersity

Jinglai Li
University of Liverpool

Linjun Lu
Shanghai Jiaotong Unviersity

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029511

An adaptive multi-fidelity PC-based ensemble Kalman inversion for inverse problems Liang Yan, Tao Zhou

Liang Yan
Southeast University of China

Tao Zhou
Chinese Academy of Sciences

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029059

Variable-separation based iterative ensemble smoother for Bayesian inverse problems in anomalous diffusion reaction models Yuming Ba, Lijian Jiang , Na Ou

Yuming Ba
Hunan University

Lijian Jiang
Tongji University

Na Ou
Hunan University

DOI: 10.1615/Int.J.UncertaintyQuantification.2019028759

A Multilevel Approach for Sequential Inference on Partially Observed Deterministic Systems Kody Law, Ajay Jasra, Yi Xu

Kody Law
University of Manchester

Ajay Jasra
NUS

Yi Xu
NUS

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027245

Using Parallel Markov Chain Monte Carlo to Quantify Uncertainties in Geothermal Reservoir Calibration Tiangang Cui, Colin Fox, Geoff Nicholls, Mike O'Sullivan

Tiangang Cui
Monash University

Colin Fox
University of Otago

Geoff Nicholls
University of Oxford

Mike O'Sullivan
The University of Auckland

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029282

An efficient numerical method for uncertainty quantification in cardiology models Xindan Gao, Wenjun Ying, Zhiwen Zhang

Xindan Gao
School of Mathematical sciences, Shanghai Jiao Tong University

Wenjun Ying
School of Mathematical sciences, Shanghai Jiao Tong University

Zhiwen Zhang
The University of Hong Kong

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027857

A General Framework for Enhancing Sparsity of Generalized Polynomial Chaos Expansions Xiu Yang, Xiaoliang Wan, Lin Lin, Lei Huan

Xiu Yang
Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory

Xiaoliang Wan
Department of Mathematics, Louisiana State University, Baton Rouge, Louisiana 70803, USA

Lin Lin
Department of Mathematics, University of California, Berkeley and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Lei Huan
Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027864

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