313 citations · 704 across the 32 of their papers we have counts for
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An augmented Lagrangian trust-region method with inexact gradient evaluations to accelerate constrained optimization problems using model hyperreduction
Tianshu Wen, Matthew J. Zahr
We present an augmented Lagrangian trust-region method to efficiently solve constrained optimization problems governed by large-scale nonlinear systems with application to partial…
A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization
Tianshu Wen, Matthew J. Zahr
We present a numerical method to efficiently solve optimization problems governed by large-scale nonlinear systems of equations, including discretized partial differential equation…
An efficient, globally convergent method for optimization under uncertainty using adaptive model reduction and sparse grids
Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri
This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages…
An Adjoint Method for a High-Order Discretization of Deforming Domain Conservation Laws for Optimization of Flow Problems
Matthew J. Zahr, Per-Olof Persson
The fully discrete adjoint equations and the corresponding adjoint method are derived for a globally high- order accurate discretization of conservation laws on parametrized, defor…
A Fully Discrete Adjoint Method for Optimization of Flow Problems on Deforming Domains with Time-Periodicity Constraints
Matthew J. Zahr, Per-Olof Persson, Jon Wilkening
A variety of shooting methods for computing fully discrete time-periodic solutions of partial differential equations, including Newton-Krylov and optimization-based methods, are di…
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…