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Fator do impacto: 0.967
FI de cinco anos: 1.301

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

Próximos artigos

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

A special issue celebrating a new UQ activity group in China -- Preface Tao Zhou

Tao Zhou
Chinese Academy of Sciences

DOI: 10.1615/Int.J.UncertaintyQuantification.2019030761

Numerical approximation of elliptic problems with log-normal random coefficients Xiaoliang Wan, Haijun Yu

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

Haijun Yu
Academy of Mathematics and Systems Science

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029046

Reducing Fracture Prediction Uncertainty Based On Time-Lapse Seismic (4D) And Deterministic Inversion Algorithm zhang Liming, Cui Chenyu, wang yi, Zhang Kai, sun zhixue, yao jun

zhang Liming
china unversity of petroleum

Cui Chenyu
china unversity of petroleum

wang yi
china unversity of petroleum

Zhang Kai
china unversity of petroleum

sun zhixue
china unversity of petroleum

yao jun
china unversity of petroleum

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027680

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

Adjoint Forward Backward Stochastic Differential Equations Driven by Jump Diffusion Processes and Its Application to Nonlinear Filtering Problems Feng Bao, yanzhao cao, Hongmei Chi

Feng Bao
Florida State University

yanzhao cao
Auburn University

Hongmei Chi
Florida A&M University

DOI: 10.1615/Int.J.UncertaintyQuantification.2019028300

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

Random regularity of a nonlinear Landau Damping solution for the Vlasov-Poisson equations with random inputs Shi Jin, Zhiyan Ding

Shi Jin
University of Wisconsin-Madison

Zhiyan Ding
University of Wisconsin-Madison

DOI: 10.1615/Int.J.UncertaintyQuantification.2019026936

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