| Nonlinear model order reduction based on local reduced‐order bases D Amsallem, MJ Zahr, C Farhat International Journal for Numerical Methods in Engineering 92 (10), 891-916, 2012 | 544 | 2012 |
| Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems H Gao, MJ Zahr, JX Wang Computer Methods in Applied Mechanics and Engineering 390, 114502, 2022 | 209 | 2022 |
| Design optimization using hyper-reduced-order models D Amsallem, M Zahr, Y Choi, C Farhat Structural and Multidisciplinary Optimization 51 (4), 919-940, 2015 | 202 | 2015 |
| Progressive construction of a parametric reduced‐order model for PDE‐constrained optimization MJ Zahr, C Farhat International Journal for Numerical Methods in Engineering 102 (5), 1111-1135, 2015 | 165 | 2015 |
| Fast local reduced basis updates for the efficient reduction of nonlinear systems with hyper-reduction D Amsallem, MJ Zahr, K Washabaugh Advances in Computational Mathematics 41, 1187-1230, 2015 | 101 | 2015 |
| Nonlinear model reduction for CFD problems using local reduced-order bases K Washabaugh, D Amsallem, M Zahr, C Farhat 42nd AIAA Fluid Dynamics Conference and Exhibit, 2686, 2012 | 96 | 2012 |
| An optimization-based approach for high-order accurate discretization of conservation laws with discontinuous solutions MJ Zahr, PO Persson Journal of Computational Physics 365, 105-134, 2018 | 76 | 2018 |
| Implicit shock tracking using an optimization-based high-order discontinuous Galerkin method MJ Zahr, A Shi, PO Persson Journal of Computational Physics 410, 109385, 2020 | 70 | 2020 |
| A multilevel projection‐based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics MJ Zahr, P Avery, C Farhat International Journal for Numerical Methods in Engineering 112 (8), 855-881, 2017 | 68 | 2017 |
| PDCO: Primal-dual interior method for convex objectives MA Saunders, B Kim, C Maes, S Akle, M Zahr Software available at http://www. stanford. edu/group/SOL/software/pdco. html, 2002 | 61 | 2002 |
| On the use of discrete nonlinear reduced-order models for the prediction of steady-state flows past parametrically deformed complex geometries KM Washabaugh, MJ Zahr, C Farhat 54th AIAA Aerospace Sciences Meeting, 1814, 2016 | 59 | 2016 |
| Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning H Gao, JX Wang, MJ Zahr Physica D: Nonlinear Phenomena 412, 132614, 2020 | 54 | 2020 |
| Blood flow imaging by optimal matching of computational fluid dynamics to 4D‐flow data J Töger, MJ Zahr, N Aristokleous, K Markenroth Bloch, M Carlsson, ... Magnetic resonance in medicine 84 (4), 2231-2245, 2020 | 47 | 2020 |
| 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 | 42 | 2019 |
| Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking MA Mirhoseini, MJ Zahr Journal of Computational Physics 473, 111739, 2023 | 41 | 2023 |
| A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows T Huang, MJ Zahr Journal of Computational Physics 454, 110981, 2022 | 34 | 2022 |
| The GNAT nonlinear model reduction method and its application to fluid dynamics problems K Carlberg, D Amsallem, P Avery, M Zahr, C Farhat 6th AIAA Theoretical Fluid Mechanics Conference, 3112, 2011 | 31 | 2011 |
| High-order, linearly stable, partitioned solvers for general multiphysics problems based on implicit–explicit Runge–Kutta schemes DZ Huang, PO Persson, MJ Zahr Computer Methods in Applied Mechanics and Engineering 346, 674-706, 2019 | 27 | 2019 |
| An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems MJ Zahr, PO Persson Journal of Computational Physics 326, 516-543, 2016 | 27 | 2016 |
| A globally convergent method to accelerate topology optimization using on-the-fly model reduction M Yano, T Huang, MJ Zahr Computer Methods in Applied Mechanics and Engineering 375, 113635, 2021 | 26 | 2021 |
Charbel FarhatVivian Church Hoff Professor in the School of Engineering, Stanford UniversityBestätigte E-Mail-Adresse bei stanford.edu