Energy‐based model order reduction for linear stochastic Galerkin systems of second order

Stochastic Galerkin methods and model order reduction for linear dynamical systems

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6 Model Reduction of Second - Order Systems

Model reduction of second order systems

where the matrix M ∈ RN×N is assumed to be invertible. Models of mechanical systems are often of this type since (1.1) then represents the equation of motion of the system. For such a system M = MT , C = CT and K = KT are respectively the mass, damping and stiffness matrices, f(t) ∈ RN×1 is the vector of external forces, and x(t) ∈ RN×1 is the vector of internal generalized coordinates (see [4]...

Padé-Type Model Reduction of Second-Order and Higher-Order Linear Dynamical Systems

A standard approach to reduced-order modeling of higher-order linear dynamical systems is to rewrite the system as an equivalent first-order system and then employ Krylov-subspace techniques for reduced-order modeling of first-order systems. While this approach results in reduced-order models that are characterized as Padé-type or even true Padé approximants of the system’s transfer function, i...

Reduction of Second Order Systems Using Second Order Krylov Subspaces

By introducing the second order Krylov subspace, a method for the reduction of second order systems is proposed leading to a reduced system of the same structure. This generalization of Krylov subspace involves two matrices and some starting vectors and the reduced order model is found by applying a projection directly to the second order model without any conversion to state space. A numerical...