Mathematical and numerical modelling of the cardiovascular system is a research topic that
has attracted remarkable interest from the mathematical community because of its intrinsic …

The computational solution of problems can be restricted by the availability of solution
methods for linear (ized) systems of equations. In conjunction with iterative methods …

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 book provides an introduction to some modern computational techniques for
optimization problems governed by partial differential equations (PDEs). The optimization …

We propose a novel approach to model viscoelasticity materials, where rate-dependent and
non-linear constitutive relationships are approximated with deep neural networks. We …

Saddle point systems arise widely in optimization problems with constraints. The utility of
Schur complement approximation is now broadly appreciated in the context of solving such …

We construct a preconditioned modified Hermitian and skew-Hermitian splitting (PMHSS)
iteration scheme for solving and preconditioning a class of block two-by-two linear systems …

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 …

Deep neural networks (DNN) can model nonlinear relations between physical quantities.
Those DNNs are embedded in physical systems described by partial differential equations …