Structure‐preserving, stability, and accuracy properties of the energy‐conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models
By Charbel Farhat, Todd Chapman, Philip Avery. 2015
305 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e6383d5e4f130dcb2011f560efe6ba68d4e1e138
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Philip Avery
- Matthew J. Zahr
- Christian Soize
- Charbel Farhat
- David Amsallem
- Kevin T. Carlberg
- Matthias Heil
- Steven L. Brunton
In Statistical and Nonlinear Physics
- Matthew J. Zahr
- Philip Avery
- Christian Soize
- Charbel Farhat
- David Amsallem
- Kevin T. Carlberg
- Andrea Manzoni
- Steven L. Brunton
In Statistics and Probability
- Charbel Farhat
- Matthew J. Zahr
- Christian Soize
- Philip Avery
- Anthony Nouy
- David Amsallem
- Rubens Sampaio
- T. Ritto
Citations in the corpus
- A multilevel projection‐based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics (2017, methodology): “Furthermore, the ECSW method distinguishes itself from alternative hyper reduction methods through its preservation of the Lagrangian structure associated with Hamilton’s principle.”
- A nonparametric probabilistic approach for quantifying uncertainties in low‐dimensional and high‐dimensional nonlinear models (2017, methodology): “caveat is remedied by equipping an MOR method with a rigorous procedure for approximating the resulting reduced operators whose computational complexity scales only with the small size n of the ROM [2, 4, 13, 14].”