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 | 734 | 2011 |
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders K Lee, KT Carlberg Journal of Computational Physics 404, 108973, 2020 | 692 | 2020 |
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 | 691 | 2013 |
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 | 311 | 2017 |
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 | 296 | 2009 |
Adaptive h-refinement for reduced-order models K Carlberg International Journal for Numerical Methods in Engineering 102 (5), 1192–1210, 2015 | 168 | 2015 |
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 | 136 | 2011 |
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 | 133 | 2015 |
Conservative model reduction for finite-volume models K Carlberg, Y Choi, S Sargsyan Journal of Computational Physics 371, 280-314, 2018 | 115 | 2018 |
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 | 102 | 2019 |
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 | 87 | 2015 |
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 | 76 | 2017 |
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 | 74 | 2019 |
A compact proper orthogonal decomposition basis for optimization-oriented reduced-order models K Carlberg, C Farhat AIAA Paper 5964, 10–12, 2008 | 63 | 2008 |
Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws K Lee, K Carlberg arXiv preprint arXiv:1909.09754, 2019 | 60 | 2019 |
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 | 56 | 2021 |
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 | 46 | 2019 |
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 | 41 | 2020 |
Model reduction of nonlinear mechanical systems via optimal projection and tensor approximation KT Carlberg Stanford University, 2011 | 40 | 2011 |
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 | 38 | 2019 |