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ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

将发表的论文

Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations Lorenc Kapllani, Long Teng, Matthias Rottmann

Lorenc Kapllani
University of Wuppertal

Long Teng
University of Wuppertal

Matthias Rottmann
University of Wuppertal

DOI: 10.1615/Int.J.UncertaintyQuantification.2024053491

Stability and Convergence of Solutions to Stochastic Inverse Problems Using Approximate Probability Densities Troy Butler, Rylan Spence, Timothy Wildey, Tian Yu Yen

Troy Butler
University of Colorado Denver, Department of Mathematical and Statistical Sciences, Denver, Colorado 80204, USA

Rylan Spence
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712, USA

Timothy Wildey
Computational Mathematics Department, Center for Computing Research, Sandia National Laboratories, Albuquerque, NM 87185

Tian Yu Yen
Sandia National Laboratories

DOI: 10.1615/Int.J.UncertaintyQuantification.2024054008

Bayesian³ Active learning for regularized arbitrary multi-element polynomial chaos using information theory Ilja Kröker, Tim Brünnette, Nils Wildt, Maria Fernanda Morales Oreamuno, Rebecca Kohlhaas, Sergey Oladyshkin, Wolfgang Nowak

Ilja Kröker
University of Stuttgart

Tim Brünnette
University of Stuttgart

Nils Wildt
University of Stuttgart

Maria Fernanda Morales Oreamuno
University of Stuttgart

Rebecca Kohlhaas
University of Stuttgart

Sergey Oladyshkin
University of Stuttgart

Wolfgang Nowak
Department of Stochastic Simulation and Safety Research for Hydrosystems (IWS/LS3),University of Stuttgart, Pfaffenwaldring 5a, Stuttgart 70569, Germany

DOI: 10.1615/Int.J.UncertaintyQuantification.2024052675

A novel probabilistic transfer learning strategy for polynomial regression Wyatt Bridgman, Uma Balakrishnan, Reese E. Jones, Jiefu Chen, Xuqing Wu, Cosmin Safta, Yueqin Huang, Mohammad Khalil

Wyatt Bridgman
Sandia National Laboratories

Uma Balakrishnan
Sandia National Laboratoriess

Reese E. Jones
Sandia National Laboratories, Livermore, California 94551, USA

Jiefu Chen
University of Houston

Xuqing Wu
University of Houston

Cosmin Safta
Sandia National Labs

Yueqin Huang
University of Houston

Mohammad Khalil
Sandia National Labs

DOI: 10.1615/Int.J.UncertaintyQuantification.2024052051

Variance-based sensitivity of Bayesian inverse problems to the prior distribution John Darges, Alen Alexanderian, Pierre Gremaud

John Darges
North Carolina State University

Alen Alexanderian
North Carolina State University

Pierre Gremaud
Department of Mathematics, NC State University

DOI: 10.1615/Int.J.UncertaintyQuantification.2024051475

Extremes of vector-valued processes by finite dimensional models Hui Xu, Mircea D. Grigoriu

Hui Xu
Cornell University

Mircea D. Grigoriu
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14850, USA

DOI: 10.1615/Int.J.UncertaintyQuantification.2024051826

Learning a class of stochastic differential equations via numerics-informed Bayesian denoising Zhanpeng Wang, Lijin Wang, Yanzhao Cao

Zhanpeng Wang
University of Chinese Academy of Sciences

Lijin Wang
University of Chinese Academy of Sciences

Yanzhao Cao
Auburn University

DOI: 10.1615/Int.J.UncertaintyQuantification.2024052020

Covariance estimation using h-statistics in Monte Carlo and multilevel Monte Carlo methods Sharana Kumar Shivanand

Sharana Kumar Shivanand
Karlsruhe Institute of Technology

DOI: 10.1615/Int.J.UncertaintyQuantification.2024051528

Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods Pierre Del Moral, Shulan Hu , Ajay Jasra, Hamza Ruzayqat, Xinyu Wang

Pierre Del Moral
Institut de Mathematiques de Bordeaux

Shulan Hu
Zhongnan University of Economics and Law

Ajay Jasra
The Chinese University of Hong Kong, Shenzhen

Hamza Ruzayqat
King Abdullah University of Science and Technology

Xinyu Wang
Zhongnan University of Economics and Law

DOI: 10.1615/Int.J.UncertaintyQuantification.2024051131

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