Model Order Reduction by Using Improved Approximation Techniques

Improved model-order reduction by using spacial information in moments

The new concept of multinode moment matching (MMM) is introduced in this paper. The MMM technique simultaneously matches the moments at several nodes of a circuit using explicit moment matching around = 0. As compared to the well known single-point moment matching (SMM) techniques (such as asymptotic waveform evaluation), MMM has several advantages. First, the number of moments required by MMM ...

Model-Order Reduction Using Interval Constraint Solving Techniques

Many natural phenomena can be modeled as ordinary or partial differential equations. A way to find solutions of such equations is to discretize them and to solve the corresponding (possibly) nonlinear large systems of equations; see (Li and Chen, 2008). Solving a large nonlinear system of equations is very computationally complex due to several numerical issues, such as high linear-algebra cost...

Model Order Reduction and Controller Design Techniques

Preconditioning Techniques in Linear Model Order Reduction

Krylov subspace methods have become very popular, not only for solving large scale linear systems, but also in the area of model order reduction. It is also well known that the performance of iterative solution techniques depends crucially on the choice of preconditioning matrix. Within the area of model order reduction, the concept of preconditioning has not yet been introduced. However, recen...

Model Order Reduction: Techniques and Tools

Model order reduction (MOR) is here understood as a computational technique to reduce the order of a dynamical system described by a set of ordinary or differential-algebraic equations (ODEs or DAEs) to facilitate or enable its simulation, the design of a controller, or optimization and design of the physical system modeled. It focuses on representing the map from inputs into the system to its ...