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Kevin Carlberg
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
K Lee, KT Carlberg
Journal of Computational Physics 404, 108973, 2020
8152020
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations
K Carlberg, C Bou‐Mosleh, C Farhat
International Journal for Numerical Methods in Engineering 86 (2), 155–181, 2011
7972011
The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
K Carlberg, C Farhat, J Cortial, D Amsallem
Journal of Computational Physics 242, 623–647, 2013
7432013
Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction
K Carlberg, M Barone, H Antil
Journal of Computational Physics 330, 693–734, 2017
3432017
A method for interpolating on manifolds structural dynamics reduced‐order models
D Amsallem, J Cortial, K Carlberg, C Farhat
International Journal for Numerical Methods in Engineering 80 (9), 1241–1258, 2009
3062009
Adaptive h-refinement for reduced-order models
K Carlberg
International Journal for Numerical Methods in Engineering 102 (5), 1192–1210, 2015
1782015
A low‐cost, goal‐oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems
K Carlberg, C Farhat
International Journal for Numerical Methods in Engineering 86 (3), 381–402, 2011
1402011
Preserving Lagrangian structure in nonlinear model reduction with application to structural dynamics
K Carlberg, R Tuminaro, P Boggs
SIAM Journal on Scientific Computing 37 (2), B153–B184, 2015
1382015
Conservative model reduction for finite-volume models
K Carlberg, Y Choi, S Sargsyan
Journal of Computational Physics 371, 280-314, 2018
1242018
Space--time least-squares Petrov--Galerkin projection for nonlinear model reduction
Y Choi, K Carlberg
SIAM Journal on Scientific Computing 41 (1), A26-A58, 2019
1142019
The ROMES method for statistical modeling of reduced-order-model error
M Drohmann, K Carlberg
SIAM/ASA Journal on Uncertainty Quantification 3 (1), 116–145, 2015
902015
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
KT Carlberg, A Jameson, MJ Kochenderfer, J Morton, L Peng, ...
Journal of Computational Physics 395, 105-124, 2019
822019
Error modeling for surrogates of dynamical systems using machine learning
S Trehan, KT Carlberg, LJ Durlofsky
International Journal for Numerical Methods in Engineering 112 (12), 1801-1827, 2017
822017
Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction
C Hoang, Y Choi, K Carlberg
Computer methods in applied mechanics and engineering 384, 113997, 2021
692021
Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws
K Lee, K Carlberg
arXiv preprint arXiv:1909.09754, 2019
662019
A compact proper orthogonal decomposition basis for optimization-oriented reduced-order models
K Carlberg, C Farhat
AIAA Paper 5964, 10–12, 2008
652008
CROM: Continuous reduced-order modeling of PDEs using implicit neural representations
PY Chen, J Xiang, DH Cho, Y Chang, GA Pershing, HT Maia, ...
arXiv preprint arXiv:2206.02607, 2022
552022
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
BA Freno, KT Carlberg
Computer Methods in Applied Mechanics and Engineering 348, 250-296, 2019
492019
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems
EJ Parish, KT Carlberg
Computer Methods in Applied Mechanics and Engineering 365, 112990, 2020
482020
An efficient, globally convergent method for optimization under uncertainty using adaptive model reduction and sparse grids
MJ Zahr, KT Carlberg, DP Kouri
SIAM/ASA Journal on Uncertainty Quantification 7 (3), 877-912, 2019
452019
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