Matthew J. Zahr

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 32

Top Topics

Conservation Laws

Partial Differential Equations

Constrained Optimization

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Jakob Vandergrift, Matthew J. Zahr
Preconditioned iterative solvers for constrained high-order implicit shock tracking methods. CoRR (2024)
Jakob Vandergrift, Matthew J. Zahr
Preconditioned iterative solvers for constrained high-order implicit shock tracking methods. J. Comput. Phys. 514 (2024)
Charles J. Naudet, Matthew J. Zahr
A space-time high-order implicit shock tracking method for shock-dominated unsteady flows. J. Comput. Phys. 501 (2024)
Julian M. Kaufmann, Matthew J. Zahr
Symmetric, Optimization-based, Cross-element Compatible Nodal Distributions for High-order Finite Elements. CoRR (2024)
Tianshu Wen, Matthew J. Zahr
An augmented Lagrangian trust-region method with inexact gradient evaluations to accelerate constrained optimization problems using model hyperreduction. CoRR (2024)
Tianci Huang, Charles J. Naudet, Matthew J. Zahr
High-order implicit shock tracking boundary conditions for flows with parametrized shocks. J. Comput. Phys. 495 (2023)
Tianci Huang, Charles J. Naudet, Matthew J. Zahr
High-order implicit shock tracking boundary conditions for flows with parametrized shocks. CoRR (2023)
Charles J. Naudet, Matthew J. Zahr
A space-time high-order implicit shock tracking method for shock-dominated unsteady flows. CoRR (2023)
Han Gao, Matthew J. Zahr
An adaptive model reduction method leveraging locally supported basis functions. CoRR (2023)
Marzieh Alireza Mirhoseini, Matthew J. Zahr
Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking. J. Comput. Phys. 473 (2023)
Victor Zucatti, Matthew J. Zahr
An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots. CoRR (2023)
Marzieh Alireza Mirhoseini, Matthew J. Zahr
Accelerated solutions of convection-dominated partial differential equations using implicit feature tracking and empirical quadrature. CoRR (2023)
Tianshu Wen, Matthew J. Zahr
A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: Application to shape optimization. J. Comput. Phys. 484 (2023)
Tianci Huang, Matthew J. Zahr
A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows. J. Comput. Phys. 454 (2022)
Andrew Shi, Per-Olof Persson, Matthew J. Zahr
Implicit shock tracking for unsteady flows by the method of lines. J. Comput. Phys. 454 (2022)
Tianshu Wen, Matthew J. Zahr
A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization. CoRR (2022)
Charles J. Naudet, Johannes Töger, Matthew J. Zahr
Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study. Eng. Comput. 38 (5) (2022)
Andrew Shi, Per-Olof Persson, Matthew J. Zahr
Implicit shock tracking for unsteady flows by the method of lines. CoRR (2021)
Marzieh Alireza Mirhoseini, Matthew J. Zahr
Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking. CoRR (2021)
Charles J. Naudet, Johannes Töger, Matthew J. Zahr
Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study. CoRR (2021)
Tianci Huang, Matthew J. Zahr
A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows. CoRR (2021)
Han Gao, Matthew J. Zahr, Jian-Xun Wang
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. CoRR (2021)
Matthew J. Zahr, Andrew Shi, Per-Olof Persson
Implicit shock tracking using an optimization-based high-order discontinuous Galerkin method. J. Comput. Phys. 410 (2020)
Daniel Z. Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr
High-order partitioned spectral deferred correction solvers for multiphysics problems. J. Comput. Phys. 412 (2020)
A. Schein, Kevin T. Carlberg, Matthew J. Zahr
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection. CoRR (2020)
Masayuki Yano, Tianci Huang, Matthew J. Zahr
A globally convergent method to accelerate topology optimization using on-the-fly model reduction. CoRR (2020)
Han Gao, Jian-Xun Wang, Matthew J. Zahr
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning. CoRR (2019)
Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids. SIAM/ASA J. Uncertain. Quantification 7 (3) (2019)
Daniel Z. Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr
High-order partitioned spectral deferred correction solvers for multiphysics problems. CoRR (2019)
Matthew J. Zahr, Andrew Shi, Per-Olof Persson
Implicit shock tracking using an optimization-based, r-adaptive, high-order discontinuous Galerkin method. CoRR (2019)
Matthew J. Zahr, Per-Olof Persson
An optimization-based approach for high-order accurate discretization of conservation laws with discontinuous solutions. J. Comput. Phys. 365 (2018)
Matthew J. Zahr, Per-Olof Persson
An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems. J. Comput. Phys. 326 (2016)
David Amsallem, Matthew J. Zahr, Kyle Washabaugh
Fast local reduced basis updates for the efficient reduction of nonlinear systems with hyper-reduction. Adv. Comput. Math. 41 (5) (2015)
Matthew J. Zahr, Charbel Farhat
Progressive construction of a parametric reduced-order model for PDE-constrained optimization. CoRR (2014)