In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered. One of the common approach in literature is to derive the first-order sufficient optimality system and to apply a finite element (FE) discretization. This leads to a specific linear but high-dimensional time variant (LTV) dynamical system. To reduce the size of the LTV system, we apply a tailored reduced order modeling technique based on empirical gramians and derived directly from the first-order optimality system. For testing purpose, we focus on two specific examples: a multiobjective optimization and a closed-loop optimal control problem. Our proposed methodology results to...
This paper presents a new reduced order model (ROM) Hessian approximation for linear-quadratic optim...
In this paper, we investigate infinite horizon optimal control problems for parametrized partial dif...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
In this paper we study the approximation of an optimal control problem for linear parabolic PDEs wit...
The main focus of this paper is on an a-posteriori analysis for different model-order strategies app...
Abstract — We present a framework to solve an optimal control problem for parabolic partial differen...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
This thesis deals with the optimal control of PDEs. After a brief introduction in the theory of elli...
In this paper, we investigate a time-limited $H_2$-model order reduction method for linear dynamical...
In this paper we study the approximation of a distributed optimal control problem for linear parabol...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/96...
In this work we deal with parametrized time dependent optimal control problems governed by partial d...
In this paper we study the approximation of an optimal control problem for linear para- bolic PDEs w...
We provide an introduction to proper orthogonal decomposition (POD) model order reduction with focus...
We propose reduced order methods as a suitable approach to face parametrized optimal control problem...
This paper presents a new reduced order model (ROM) Hessian approximation for linear-quadratic optim...
In this paper, we investigate infinite horizon optimal control problems for parametrized partial dif...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...
In this paper we study the approximation of an optimal control problem for linear parabolic PDEs wit...
The main focus of this paper is on an a-posteriori analysis for different model-order strategies app...
Abstract — We present a framework to solve an optimal control problem for parabolic partial differen...
The optimization and control of systems governed by partial differential equations (PDEs) usually re...
This thesis deals with the optimal control of PDEs. After a brief introduction in the theory of elli...
In this paper, we investigate a time-limited $H_2$-model order reduction method for linear dynamical...
In this paper we study the approximation of a distributed optimal control problem for linear parabol...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/96...
In this work we deal with parametrized time dependent optimal control problems governed by partial d...
In this paper we study the approximation of an optimal control problem for linear para- bolic PDEs w...
We provide an introduction to proper orthogonal decomposition (POD) model order reduction with focus...
We propose reduced order methods as a suitable approach to face parametrized optimal control problem...
This paper presents a new reduced order model (ROM) Hessian approximation for linear-quadratic optim...
In this paper, we investigate infinite horizon optimal control problems for parametrized partial dif...
An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is ...