Add to ORKGIn 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
The optimization of functions subject to partial differential equations (PDE) plays an important rol...
Design optimization problems are often formulated as an optimization problem whose objective is a fu...
Optimization problems subject to constraints governed by partial differential equations (PDEs) are a...
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...
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...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Parameter optimization problems constrained by partial differential equations (PDEs) appear in many ...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
Optimization problems such as the parameter design of dynamical systems are often computationally ex...
This thesis is concerned with the development, analysis and implementation of efficient reduced orde...
The optimization of functions subject to partial differential equations (PDE) plays an important rol...
Design optimization problems are often formulated as an optimization problem whose objective is a fu...
Optimization problems subject to constraints governed by partial differential equations (PDEs) are a...
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...
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...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Design optimization problems are often formulated as PDE-constrained optimization problems where the...
Parameter optimization problems constrained by partial differential equations (PDEs) appear in many ...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
Optimization problems such as the parameter design of dynamical systems are often computationally ex...
This thesis is concerned with the development, analysis and implementation of efficient reduced orde...
The optimization of functions subject to partial differential equations (PDE) plays an important rol...
Design optimization problems are often formulated as an optimization problem whose objective is a fu...
Optimization problems subject to constraints governed by partial differential equations (PDEs) are a...