Second-Order PDE Constrained Controlled Optimization Problems with Application in Mechanics

Second-order adjoints for solving PDE-constrained optimization problems

Inverse problems are of utmost importance in many fields of science and engineering. In the variational approach inverse problems are formulated as PDE-constrained optimization problems, where the optimal estimate of the uncertain parameters is the minimizer of a certain cost functional subject to the constraints posed by the model equations. The numerical solution of such optimization problems...

Model Problems in PDE-Constrained Optimization

This work aims to aid in introducing, experimenting and benchmarking algorithms for PDE-constrained optimization problems by presenting a set of such model problems. We specifically examine a type of PDE-constrained optimization problem, the parameter estimation problem. We present three model parameter estimation problems, each containing a different type of partial differential equation as th...

Constrained Programming for Optimization Problems in PDE

Optimization problems in PDE models are often approached by considering the PDE model as a black-box input-output relation and thus solving an unconstrained optimization problem. In contrast to that, considering the PDE model as a side condition of a resulting constrained programming problem enables us to simultaneously solve the PDE model equations together with the optimization problem. This ...

Model Order Reduction for PDE Constrained Optimization

The optimization and control of systems governed by partial differential equations (PDEs) usually requires numerous evaluations of the forward problem or the optimality system. Despite the fact that many recent efforts, many of which are reported in this book, have been made to limit or reduce the number of evaluations to 5–10, this cannot be achieved in all situations and even if this is possi...

Reduced-Order Galerkin Approximations in PDE Constrained Optimization