Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics-Reference-Cited by-同舟云学术

Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics

Published:2018-11-14 Issue:7 Volume:117 Page:819-843
ISSN:0029-5981
Container-title:International Journal for Numerical Methods in Engineering
language:en
Short-container-title:Int J Numer Methods Eng

Author:

Soize C.¹^ORCID,Farhat C.²^ORCID

Affiliation:

1. Laboratoire Modélisation et Simulation Multi Echelle MSME UMR 8208 CNRS, Université Paris‐Est Marne‐la‐Vallee France

2. Department of Aeronautics and Astronautics Department of Mechanical Engineering, and Institute for Computational and Mathematical Engineering, Stanford University Stanford California

Funder

Army Research Laboratory

Boeing

Defense Advanced Research Projects Agency

Publisher

Wiley

Subject

Applied Mathematics,General Engineering,Numerical Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.5980

Reference24 articles.

1. An adaptive and efficient greedy procedure for the optimal training of parametric reduced-order models

2. Truncated SVD methods for discrete linear ill-posed problems

3. Structure-preserving, stability, and accuracy properties of the energy-conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models

4. A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models

5. Modeling and Quantification of Model-Form Uncertainties in Eigenvalue Computations Using a Stochastic Reduced Model

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