Eighty years of the finite element method: Birth, evolution, and future

WK Liu, S Li, HS Park - Archives of Computational Methods in …, 2022 - Springer
This document presents comprehensive historical accounts on the developments of finite
element methods (FEM) since 1941, with a specific emphasis on developments related to …

On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward

HS Choi, C Crump, C Duriez… - Proceedings of the …, 2021 - National Acad Sciences
The last five years marked a surge in interest for and use of smart robots, which operate in
dynamic and unstructured environments and might interact with humans. We posit that well …

Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction

J Barnett, C Farhat - Journal of Computational Physics, 2022 - Elsevier
A quadratic approximation manifold is presented for performing nonlinear, projection-based,
model order reduction (PMOR). It constitutes a departure from the traditional affine subspace …

[图书][B] Uncertainty quantification

C Soize - 2017 - Springer
This book results from a course developed by the author and reflects both his own and
collaborative research regarding the development and implementation of uncertainty …

Fast, generic, and reliable control and simulation of soft robots using model order reduction

O Goury, C Duriez - IEEE Transactions on Robotics, 2018 - ieeexplore.ieee.org
Obtaining an accurate mechanical model of a soft deformable robot compatible with the
computation time imposed by robotic applications is often considered an unattainable goal …

Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

K Carlberg, M Barone, H Antil - Journal of Computational Physics, 2017 - Elsevier
Abstract Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …

Structure‐preserving, stability, and accuracy properties of the energy‐conserving sampling and weighting method for the hyper reduction of nonlinear finite element …

C Farhat, T Chapman, P Avery - International journal for …, 2015 - Wiley Online Library
The computational efficiency of a typical, projection‐based, nonlinear model reduction
method hinges on the efficient approximation, for explicit computations, of the scalar …

Neural-network-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility

J Barnett, C Farhat, Y Maday - Journal of Computational Physics, 2023 - Elsevier
Inspired by our previous work on a quadratic approximation manifold [1], we propose in this
paper a computationally tractable approach for combining a projection-based reduced-order …

[HTML][HTML] Dimensional hyper-reduction of nonlinear finite element models via empirical cubature

JA Hernandez, MA Caicedo, A Ferrer - Computer methods in applied …, 2017 - Elsevier
We present a general framework for the dimensional reduction, in terms of number of
degrees of freedom as well as number of integration points (“hyper-reduction”), of nonlinear …

On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows

S Grimberg, C Farhat, N Youkilis - Journal of Computational Physics, 2020 - Elsevier
In the literature on nonlinear projection-based model order reduction for computational fluid
dynamics problems, it is often claimed that due to modal truncation, a projection-based …