Partial differential equations (PDEs) are indispensable for modeling many physical
phenomena and also commonly used for solving image processing tasks. In the latter area …

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 …

Chemical engineering applications have been a source of challenging optimization
problems for over 50 years. For many chemical process systems, detailed steady state and …

The objective of this monograph is the treatment of a general class of nonlinear variational
problems of the form min y∈ Y, u∈ U ƒ (y, u) subject to e (y, u)= 0, g (y, u)∈ K, 0.0. 1 where …

Solving large-scale PDE-constrained optimization problems presents computational
challenges due to the large dimensional set of underlying equations that have to be handled …

In this work, we establish the relation between optimal control and training deep Convolution
Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted …

We present a method for optimal control of systems governed by partial differential
equations (PDEs) with uncertain parameter fields. We consider an objective function that …

Often, parameter estimation problems of parameter-dependent PDEs involve multiple right-
hand sides. The computational cost and memory requirements of such problems increase …

In clinical applications of magnetic nanoparticle hyperthermia for cancer treatment it is very
important to ensure a maximum damage to the tumor while protecting the normal tissue. The …

This paper presents a distributed magnetotelluric inversion scheme based on adaptive finite-
element method (FEM). The key novel aspect of the introduced algorithm is the use of …