| Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders K Lee, KT Carlberg Journal of Computational Physics 404, 108973, 2020 | 827 | 2020 |
| 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 | 801 | 2011 |
| 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 | 748 | 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 | 344 | 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 | 307 | 2009 |
| Adaptive h-refinement for reduced-order models K Carlberg International Journal for Numerical Methods in Engineering 102 (5), 1192–1210, 2015 | 179 | 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 | 140 | 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 | 139 | 2015 |
| Conservative model reduction for finite-volume models K Carlberg, Y Choi, S Sargsyan Journal of Computational Physics 371, 280-314, 2018 | 124 | 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 | 115 | 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 | 90 | 2015 |
| 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 | 84 | 2019 |
| 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 | 82 | 2017 |
| 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 | 70 | 2021 |
| Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws K Lee, K Carlberg arXiv preprint arXiv:1909.09754, 2019 | 66 | 2019 |
| A compact proper orthogonal decomposition basis for optimization-oriented reduced-order models K Carlberg, C Farhat AIAA Paper 5964, 10–12, 2008 | 65 | 2008 |
| 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 | 56 | 2022 |
| 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 | 49 | 2020 |
| 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 | 49 | 2019 |
| 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 | 46 | 2019 |
Charbel FarhatVivian Church Hoff Professor in the School of Engineering, Stanford UniversityVerified email at stanford.edu
