Uncertainty quantification appears today as a crucial point in numerous branches of science
and engineering. In the last two decades, a growing interest has been devoted to a new …

Model reduction methods are usually based on preliminary computations to build the shape
function of the reduced order model (ROM) before the computation of the reduced state …

Many problems in science and engineering require the solution of a long sequence of slowly
changing linear systems. We propose and analyze two methods that significantly reduce the …

The modern design of industrial structures leads to very complex simulations characterized
by nonlinearities, high heterogeneities, tortuous geometries... Whatever the modelization …

This article describes a bridge between POD-based model order reduction techniques and
the classical Newton/Krylov solvers. This bridge is used to derive an efficient algorithm to …

This survey concerns subspace recycling methods, a popular class of iterative methods that
enable effective reuse of subspace information in order to speed up convergence and find …

This paper proposes a novel technique to reduce the computational burden associated with
the simulation of localized failure. The proposed methodology affords the simulation of …

We discuss the efficient solution of a long sequence of slowly varying linear systems arising
in computations for diffuse optical tomographic imaging. The reconstruction of three …

Although design optimization has shown its great power of automatizing the whole design
process and providing an optimal design, using sophisticated computational models, its …

In this paper, we explore the capabilities of some nonlinear strategies based on domain
decomposition for nonlinear analyses, and more particularly for post-buckling analyses of …