In PDE constrained optimization, physical parameters need to be determined so that some objective function is minimized. We assume here an objective function that depends on the output of a dynamical system, modeled by a discretized PDE. Krylov-Pade model reduction for computing the output can significantly decrease the computation time. In addition, gradients are well approximated, which allows using gradient based optimization on the reduced model. We show numerical results for different methods embedded in line search and trust region methods for benchmark problems from structural engineering.status: publishe
We investigate an optimization problem governed by an elliptic partial differential equation with un...
Computational modeling research centers around developing ever better representations of physics. Th...
This volume provides a broad and uniform introduction of PDE-constrained optimization as well as to ...
In PDE constrained optimization, physical parameters need to be determined so that some objective fu...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Optimization problems such as the parameter design of dynamical systems are often computationally ex...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
Presents an introduction of pde constrained optimization. This book provides a precise functional an...
Optimization problems subject to constraints governed by partial differential equations (PDEs) are a...
We investigate an optimization problem governed by an elliptic partial differential equation with un...
Computational modeling research centers around developing ever better representations of physics. Th...
This volume provides a broad and uniform introduction of PDE-constrained optimization as well as to ...
In PDE constrained optimization, physical parameters need to be determined so that some objective fu...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Optimization problems such as the parameter design of dynamical systems are often computationally ex...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
In many engineering problems, the behavior of dynamical systems depends on physical parameters. In d...
Presents an introduction of pde constrained optimization. This book provides a precise functional an...
Optimization problems subject to constraints governed by partial differential equations (PDEs) are a...
We investigate an optimization problem governed by an elliptic partial differential equation with un...
Computational modeling research centers around developing ever better representations of physics. Th...
This volume provides a broad and uniform introduction of PDE-constrained optimization as well as to ...