Matthew Zahr
Cited by
Cited by
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
Design optimization using hyper-reduced-order models
D Amsallem, M Zahr, Y Choi, C Farhat
Structural and Multidisciplinary Optimization 51 (4), 919-940, 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
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 (5), 1187-1230, 2015
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
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
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
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
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
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
PDCO: Primal-Dual interior method for Convex Objectives, 2003
M Saunders, B Kim, C Maes, S Akle, M Zahr
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
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
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
Construction of parametrically-robust CFD-based reduced-order models for PDE-constrained optimization
MJ Zahr, D Amsallem, C Farhat
21st AIAA Computational Fluid Dynamics Conference, 2845, 2013
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
Adaptive model reduction to accelerate optimization problems governed by partial differential equations
MJ Zahr
Stanford University, 2016
A fully discrete adjoint method for optimization of flow problems on deforming domains with time-periodicity constraints
MJ Zahr, PO Persson, J Wilkening
Computers & Fluids 139, 130-147, 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
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
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