activity
20192021
most citedPreserving general physical properties in model reduction of dynamical systems via constrained-optimization projection

13 citations · 13 across the 3 of their papers we have counts for

collaborators

7 papers

math.NA2021

Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking

Marzieh Alireza Mirhoseini, Matthew J. Zahr

This work introduces a new approach to reduce the computational cost of solving partial differential equations (PDEs) with convection-dominated solutions: model reduction with impl…

math.NA2021

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Charles J. Naudet, Johannes Toger, Matthew J. Zahr

Simulation-based imaging (SBI) is a blood flow imaging technique that optimally fits a computational fluid dynamics (CFD) simulation to low-resolution, noisy magnetic resonance (MR…

cs.CE202013 cited

Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection

A. Schein, K. T. Carlberg, M. J. Zahr

Model-reduction techniques aim to reduce the computational complexity of simulating dynamical systems by applying a (Petrov-)Galerkin projection process that enforces the dynamics…

math.NA2020

A globally convergent method to accelerate topology optimization using on-the-fly model reduction

Masayuki Yano, Tianci Huang, Matthew J. Zahr

We present a globally convergent method to accelerate density-based topology optimization using projection-based reduced-order models (ROMs) and trust-region methods. To accelerate…

math.NA2019

Implicit shock tracking using an optimization-based high-order discontinuous Galerkin method

Matthew J. Zahr, Andrew Shi, Per-Olof Persson

A novel framework for resolving discontinuous solutions of conservation laws, e.g., contact lines, shock waves, and interfaces, using implicit tracking and a high-order discontinuo…

math.NA2019

Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning

Han Gao, Jian-Xun Wang, Matthew J. Zahr

Projection-based model reduction has become a popular approach to reduce the cost associated with integrating large-scale dynamical systems so they can be used in many-query settin…