2 citations · 2 across the 3 of their papers we have counts for
3 papers
math.NA2023
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…
math.NA2023★ 2 cited
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…
math.OC2014
Progressive construction of a parametric reduced-order model for PDE-constrained optimization
Matthew J. Zahr, Charbel Farhat
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is introduced that breaks the traditional offline-online framework of model order r…