In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and
wide applicability for simulating large-scale mathematical models in engineering and the …

A structure-preserving dimension reduction algorithm for large-scale second-order
dynamical systems is presented. It is a projection method based on a second-order Krylov …

In this first section we present a high level discussion on computational science, and the
need for compact models of phenomena observed in nature and industry. We argue that …

The reduced basis method (RBM) generates low-order models for the solution of
parametrized partial differential equations to allow for efficient evaluation in many-query and …

The second-order Arnoldi (SOAR) procedure is an algorithm for computing an orthonormal
basis of the second-order Krylov subspace. It has found applications in solving quadratic …

A general framework for structure-preserving model reduction by Krylov subspace projection
methods is developed. It not only matches as many moments as possible but also preserves …

Higher-level representations (macromodels, reduced-order models) abstract away
unnecessary implementation details and model only important system properties such as …

We review some recent extensions of the Finite Integration Technique (FIT), which is known
to be a generalization of the Finite Difference Time Domain (FDTD) method. Some …

We present a new approach for generating fast simulation models of electromagnetic (EM)
devices that contain moving components and magnetic materials with nonlinear properties …

This paper examines classical Model Order Reduction (MOR) strategies in view of the
particular properties and needs of computational electromagnetism. Hereby reduced models …