This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

This book is a description of why and how to do Scientific Computing for fundamental
models of fluid flow. It contains introduction, motivation, analysis, and algorithms and is …

This is a book on Optimal Control Problems (OCPs): how to formulate them, how to set up a
suitable mathematical framework for their analysis, how to approximate them numerically …

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 …

In this paper we discuss how to find norms for parameter-dependent saddle point problems
which lead to robust (ie, parameter-independent) estimates of the solution in terms of the …

Saddle point matrices of a special structure arise in optimal control problems. In this paper
we consider distributed optimal control for various types of scalar stationary partial …

The solution of time-dependent PDE-constrained optimization problems subject to unsteady
flow equations presents a challenge to both algorithms and computing power. In this paper …

In this paper we develop and analyze a multilevel weighted reduced basis method for
solving stochastic optimal control problems constrained by Stokes equations. We prove the …

In this article we construct an efficient preconditioner for solving the algebraic systems
arising from discretized optimal control problems with time-periodic Stokes equations, based …

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 …