Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

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 …

In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

The simulation of electronic circuits involves the numerical solution of very large-scale,
sparse, in general nonlinear, systems of differential-algebraic equations. Often, the size of …

In recent years, reduced-order modelling techniques based on Krylov-subspace iterations,
especially the Lanczos algorithm and the Arnoldi process, have become popular tools for …

Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …

Large scale dynamical systems are a common framework for the modeling and control of
many complex phenomena of scientific interest and industrial value, with examples of …

The paper describes the recent state of the art in hierarchical analog synthesis, with a strong
emphasis on associated techniques for computer-aided model generation and optimization …

In recent years, order-reduction techniques based on Krylov subspaces have become the
methods of choice for generating macromodels of large multi-port RLC circuits. A widely …

In recent years, reduced-order modeling techniques based on Krylov-subspace iterations,
especially the Lanczos algorithm and the Arnoldi process, have become popular tools to …