In this article, we motivate, derive, and test effective preconditioners to be used with the
Minres algorithm for solving a number of saddle point systems which arise in PDE …

We propose and compare methods for the analysis of extreme events in complex systems
governed by PDEs that involve random parameters, in situations where we are interested in …

We propose the reduced basis method for the solution of parametrized optimal control
problems described by parabolic partial differential equations in the unconstrained case …

High fidelity models used in many science and engineering applications couple multiple
physical states and parameters. Inverse problems arise when a model parameter cannot be …

We consider the calibration of parameters in physical models described by partial differential
equations. This task is formulated as a constrained optimization problem with a cost …

Many problems in engineering and sciences require the solution of large scale optimization
constrained by partial differential equations (PDEs). Though PDE-constrained optimization …

A common goal throughout science and engineering is to solve optimization problems
constrained by computational models. However, in many cases a high-fidelity numerical …

PDE-constrained optimization problems, and the development of preconditioned iterative
methods for the efficient solution of the arising matrix systems, is a field of numerical analysis …

Abstract We propose a Reduced Basis method for the solution of parametrized optimal
control problems with control constraints for which we extend the method proposed in Dedè …

We consider hyper-differential sensitivity analysis (HDSA) of nonlinear Bayesian inverse
problems governed by partial differential equations (PDEs) with infinite-dimensional …