Forthcoming Articles

Inverse Uncertainty Quantification of a cell model using a Gaussian Process metamodel Kevin de Vries, Anna Nikishova, Benjamin Czaja, Gábor Závodszky, Alfons Hoekstra

Kevin de Vries
University of Amsterdam

Anna Nikishova
University of Amsterdam

Benjamin Czaja
University of Amsterdam

Gábor Závodszky
University of Amsterdam

Alfons Hoekstra
University of Amsterdam

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033186

Data-Driven Calibration of P3D Hydraulic Fracturing Models Souleymane Zio, Fernando A. Rochinha

Souleymane Zio
Ecole Polytechnique de Ouagadougou

Fernando A. Rochinha
COPPE, Universidade Federaldo Rio de Janeiro Brazil

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033602

A multi-fidelity neural network surrogate sampling method for uncertainty quantification Mohammad Motamed

Mohammad Motamed
University of New Mexico

DOI: 10.1615/Int.J.UncertaintyQuantification.2020031957

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

Sensitivity indices for output on a Riemannian manifold Ricardo Fraiman, Fabrice Gamboa, Leonardo Moreno

Ricardo Fraiman
Centro de Matemática, Facultad de Ciencias, Universidad de la República, Uruguay

Fabrice Gamboa
Institut de Mathématiques de Toulouse, France

Leonardo Moreno
Departamento de Métodos Cuantitativos, Facultad de Ciencias Económicas y de Administración, Universidad de la República, Uruguay

DOI: 10.1615/Int.J.UncertaintyQuantification.2020029614

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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