Matthew J. Zahr

43 papers Journal 43

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A sharp-interface discontinuous Galerkin method for simulation of two-phase flow of real gases based on implicit shock tracking. J. Comput. Phys. Charles J. Naudet, Brian Taylor, Matthew J. Zahr
2026	J	`jnl`	An implicit shock tracking method for simulation of shock-dominated flows over complex domains using mesh-based parametrizations. J. Comput. Phys. Alexander M. Perez Reyes, Matthew J. Zahr
2025	J	`jnl`	A sharp-interface discontinuous Galerkin method for simulation of two-phase flow of real gases based on implicit shock tracking. CoRR Charles J. Naudet, Brian Taylor, Matthew J. Zahr
2025	J	`jnl`	An CoRR Huijing Dong, Masayuki Yano, Tianci Huang, Matthew J. Zahr
2025	J	`jnl`	An implicit shock tracking method for simulation of shock-dominated flows over complex domains using mesh-based parametrizations. CoRR Alexander M. Perez Reyes, Matthew J. Zahr
2025	J	`jnl`	An rp-adaptive method for accurate resolution of shock-dominated viscous flow based on implicit shock tracking. J. Comput. Phys. Huijing Dong, Masayuki Yano, Tianci Huang, Matthew J. Zahr
2025	J	`jnl`	Model reduction of convection-dominated viscous conservation laws using implicit feature tracking and landmark image registration. CoRR Victor Zucatti, Matthew J. Zahr
2025	J	`jnl`	Neural network-based Godunov corrections for approximate Riemann solvers using bi-fidelity learning. CoRR Akshay Thakur, Matthew J. Zahr
2025	J	`jnl`	Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems. CoRR Akshay Thakur, Sawan Kumar, Matthew J. Zahr, Souvik Chakraborty
2024	J	`jnl`	A space-time high-order implicit shock tracking method for shock-dominated unsteady flows. J. Comput. Phys. Charles J. Naudet, Matthew J. Zahr
2024	J	`jnl`	An augmented Lagrangian trust-region method with inexact gradient evaluations to accelerate constrained optimization problems using model hyperreduction. CoRR Tianshu Wen, Matthew J. Zahr
2024	J	`jnl`	Preconditioned iterative solvers for constrained high-order implicit shock tracking methods. CoRR Jakob Vandergrift, Matthew J. Zahr
2024	J	`jnl`	Preconditioned iterative solvers for constrained high-order implicit shock tracking methods. J. Comput. Phys. Jakob Vandergrift, Matthew J. Zahr
2024	J	`jnl`	Symmetric, Optimization-based, Cross-element Compatible Nodal Distributions for High-order Finite Elements. CoRR Julian M. Kaufmann, Matthew J. Zahr
2023	J	`jnl`	A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: Application to shape optimization. J. Comput. Phys. Tianshu Wen, Matthew J. Zahr
2023	J	`jnl`	A space-time high-order implicit shock tracking method for shock-dominated unsteady flows. CoRR Charles J. Naudet, Matthew J. Zahr
2023	J	`jnl`	Accelerated solutions of convection-dominated partial differential equations using implicit feature tracking and empirical quadrature. CoRR Marzieh Alireza Mirhoseini, Matthew J. Zahr
2023	J	`jnl`	An adaptive model reduction method leveraging locally supported basis functions. CoRR Han Gao, Matthew J. Zahr
2023	J	`jnl`	An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots. CoRR Victor Zucatti, Matthew J. Zahr
2023	J	`jnl`	High-order implicit shock tracking boundary conditions for flows with parametrized shocks. CoRR Tianci Huang, Charles J. Naudet, Matthew J. Zahr
2023	J	`jnl`	High-order implicit shock tracking boundary conditions for flows with parametrized shocks. J. Comput. Phys. Tianci Huang, Charles J. Naudet, Matthew J. Zahr
2023	J	`jnl`	Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking. J. Comput. Phys. Marzieh Alireza Mirhoseini, Matthew J. Zahr
2022	J	`jnl`	A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization. CoRR Tianshu Wen, Matthew J. Zahr
2022	J	`jnl`	A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows. J. Comput. Phys. Tianci Huang, Matthew J. Zahr
2022	J	`jnl`	Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study. Eng. Comput. Charles J. Naudet, Johannes Töger, Matthew J. Zahr
2022	J	`jnl`	Implicit shock tracking for unsteady flows by the method of lines. J. Comput. Phys. Andrew Shi, Per-Olof Persson, Matthew J. Zahr
2021	J	`jnl`	A robust, high-order implicit shock tracking method for simulation of complex, high-speed flows. CoRR Tianci Huang, Matthew J. Zahr
2021	J	`jnl`	Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study. CoRR Charles J. Naudet, Johannes Töger, Matthew J. Zahr
2021	J	`jnl`	Implicit shock tracking for unsteady flows by the method of lines. CoRR Andrew Shi, Per-Olof Persson, Matthew J. Zahr
2021	J	`jnl`	Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking. CoRR Marzieh Alireza Mirhoseini, Matthew J. Zahr
2021	J	`jnl`	Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. CoRR Han Gao, Matthew J. Zahr, Jian-Xun Wang
2020	J	`jnl`	A globally convergent method to accelerate topology optimization using on-the-fly model reduction. CoRR Masayuki Yano, Tianci Huang, Matthew J. Zahr
2020	J	`jnl`	High-order partitioned spectral deferred correction solvers for multiphysics problems. J. Comput. Phys. Daniel Zhengyu Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr
2020	J	`jnl`	Implicit shock tracking using an optimization-based high-order discontinuous Galerkin method. J. Comput. Phys. Matthew J. Zahr, Andrew Shi, Per-Olof Persson
2020	J	`jnl`	Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection. CoRR Alexander Schein, Kevin T. Carlberg, Matthew J. Zahr
2019	J	`jnl`	An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids. SIAM/ASA J. Uncertain. Quantification Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri
2019	J	`jnl`	High-order partitioned spectral deferred correction solvers for multiphysics problems. CoRR Daniel Zhengyu Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr
2019	J	`jnl`	Implicit shock tracking using an optimization-based, r-adaptive, high-order discontinuous Galerkin method. CoRR Matthew J. Zahr, Andrew Shi, Per-Olof Persson
2019	J	`jnl`	Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning. CoRR Han Gao, Jian-Xun Wang, Matthew J. Zahr
2018	J	`jnl`	An optimization-based approach for high-order accurate discretization of conservation laws with discontinuous solutions. J. Comput. Phys. Matthew J. Zahr, Per-Olof Persson
2016	J	`jnl`	An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems. J. Comput. Phys. Matthew J. Zahr, Per-Olof Persson
2015	J	`jnl`	Fast local reduced basis updates for the efficient reduction of nonlinear systems with hyper-reduction. Adv. Comput. Math. David Amsallem, Matthew J. Zahr, Kyle Washabaugh
2014	J	`jnl`	Progressive construction of a parametric reduced-order model for PDE-constrained optimization. CoRR Matthew J. Zahr, Charbel Farhat