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Fator do impacto: 4.911
FI de cinco anos: 3.179

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

Próximos artigos

MFNets: Multi-fidelity data-driven networks for Bayesian learning and prediction Alex Gorodetsky, John D. Jakeman, Gianluca Geraci, Michael S. Eldred

Alex Gorodetsky
University of Michigan

John D. Jakeman
Sandia National Laboratories, Albuquerque, NM, USA

Gianluca Geraci
Sandia National Laboratories

Michael S. Eldred
Sandia National Laboratories, P. O. Box 5800, Mail Stop: 1318, Org: 01411, Albuquerque, NM 87185-1318, USA

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032978

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo Thomas Catanach, Huy Vo, Brian Munsky

Thomas Catanach
Sandia National Laboratories

Huy Vo
Colorado State University

Brian Munsky
Colorado State University

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033241

Variance reduction methods and multilevel Monte Carlo strategy for estimating densities of solutions to random second order linear differential equations Marc Jornet Sanz, Julia Calatayud Gregori, Olivier P. Le Maître, Juan Carlos Cortés López

Marc Jornet Sanz
Universitat Politècnica de València

Julia Calatayud Gregori
Universitat Politècnica de València

Olivier P. Le Maître
École Polytechnique, Palaiseau, France

Juan Carlos Cortés López
Universitat Politècnica de València

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032659

On Transfer Learning of Neural Networks using Bi-fidelity Data for Uncertainty Propagatio Subhayan De, Jolene Britton, Matthew Reynolds, Ryan Skinner, Kenneth Jansen, Alireza Doostan

Subhayan De
University of Colorado Boulder

Jolene Britton
University of California, Riverside

Matthew Reynolds
National Renewable Energy Laboratory

Ryan Skinner
University of Colorado Boulder

Kenneth Jansen
University of Colorado Boulder

Alireza Doostan
CU Boulder

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033267

Computation of Sobol indices in global sensitivity analysis from small data sets by probabilistic learning on manifolds Maarten Arnst, Christian Soize, Kevin Bulthuis

Maarten Arnst
University of Liege

Christian Soize
Université Paris-Est Marne-la-Vallée

Kevin Bulthuis
University of Liege

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032674

Stochastic Spectral Embedding Stefano Marelli, Paul-Remo Wagner, Christos Lataniotis, Bruno Sudret

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

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

Christos Lataniotis
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.2020034395

MULTI-FIDELITY ESTIMATORS FOR CORONARY CIRCULATION MODELS UNDER CLINICALLY-INFORMED DATA UNCERTAINTY Jongmin Seo, Casey Fleeter, Andrew Kahn , Alison Marsden, Daniele Schiavazzi

Jongmin Seo
Stanford University

Casey Fleeter
Stanford University

Andrew Kahn
University of California, San Diego

Alison Marsden
Stanford University

Daniele Schiavazzi
University of Notre Dame

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033068

Multifidelity Modeling of Irradiated Particle-Laden Turbulence Subject to Uncertainty Lluis Jofre, Manolis Papadakis, Pamphile Roy, Alex Aiken, Gianluca Iaccarino

Lluis Jofre
Technical University of Catalonia - BarcelonaTech

Manolis Papadakis
Stanford University

Pamphile Roy
CERFACS

Alex Aiken
Stanford University

Gianluca Iaccarino
Stanford University

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032236

Multi-Fidelity modeling of Probabilistic Aerodynamic Databases for Use in Aerospace Engineering Jayant Mukhopadhaya, Brian Whitehead, John Quindlen, Juan Alonso, Andrew Cary

Jayant Mukhopadhaya
Stanford University

Brian Whitehead
The Boeing Company

John Quindlen
The Boeing Company

Juan Alonso
Stanford University

Andrew Cary
The Boeing Company

DOI: 10.1615/Int.J.UncertaintyQuantification.2020032841

Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method Lukas Bruder, Michael Gee, Timothy Wildey

Lukas Bruder
TU-Munich

Michael Gee
TU-Munich

Timothy Wildey
Sandia National Labs

DOI: 10.1615/Int.J.UncertaintyQuantification.2020030092

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