313 citations · 704 across the 32 of their papers we have counts for
5 papers · 1 filter
An adaptive model reduction method leveraging locally supported basis functions
Han Gao, Matthew J. Zahr
We propose a new method, the continuous Galerkin method with globally and locally supported basis functions (CG-GL), to address the parametric robustness issues of reduced-order mo…
A space-time high-order implicit shock tracking method for shock-dominated unsteady flows
Charles J. Naudet, Matthew J. Zahr
High-order implicit shock tracking (fitting) is a class of high-order, optimization-based numerical methods to approximate solutions of conservation laws with non-smooth features b…
Accelerated solutions of convection-dominated partial differential equations using implicit feature tracking and empirical quadrature
Marzieh Alireza Mirhoseini, Matthew J. Zahr
This work introduces an empirical quadrature-based hyperreduction procedure and greedy training algorithm to effectively reduce the computational cost of solving convection-dominat…
High-order implicit shock tracking boundary conditions for flows with parametrized shocks
Tianci Huang, Charles Naudet, Matthew J. Zahr
High-order implicit shock tracking (fitting) is a class of high-order, optimization-based numerical methods to approximate solutions of conservation laws with non-smooth features b…
An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots
Victor Zucatti, Matthew J. Zahr
The vast majority of reduced-order models (ROMs) first obtain a low dimensional representation of the problem from high-dimensional model (HDM) training data which is afterwards us…