Comparison of Model Reduction Methods with Applications to Circuit Simulation

Scientific Computing in Electrical Engineering - Mathematics in Industry ◽

10.1007/978-3-540-71980-9_1 ◽

2007 ◽

pp. 3-24 ◽

Cited By ~ 5

Author(s):

Roxana Ionutiu ◽

Sanda Lefteriu ◽

Athanasios C. Antoulas

Keyword(s):

Model Reduction ◽

Circuit Simulation ◽

Reduction Methods

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Model reduction methods based on Krylov subspaces

Acta Numerica ◽

10.1017/s0962492902000120 ◽

2003 ◽

Vol 12 ◽

pp. 267-319 ◽

Cited By ~ 178

Author(s):

Roland W. Freund

Keyword(s):

Large Scale ◽

Krylov Subspace ◽

Circuit Simulation ◽

Krylov Subspaces ◽

Lanczos Algorithm ◽

Linear Dynamical Systems ◽

Reduced Order ◽

Reduced Order Modelling ◽

Reduction Methods ◽

Modelling Techniques

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 tackling the large-scale time-invariant linear dynamical systems that arise in the simulation of electronic circuits. This paper reviews the main ideas of reduced-order modelling techniques based on Krylov subspaces and describes some applications of reduced-order modelling in circuit simulation.

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Implementing model reduction to the JuliaFEM platform

Rakenteiden Mekaniikka ◽

10.23998/rm.69026 ◽

2018 ◽

Vol 51 (1) ◽

pp. 36-54 ◽

Cited By ~ 1

Author(s):

Marja Liisa Rapo ◽

Jukka Aho ◽

Hannu Koivurova ◽

Tero Frondelius

Keyword(s):

Finite Element Method ◽

Finite Element ◽

Model Reduction ◽

Open Source ◽

Degrees Of Freedom ◽

Mass Matrices ◽

Dynamic Condensation ◽

Dynamic Analyses ◽

Reduction Methods ◽

Large Model

JuliaFEM is an open source finite element method solver written in the Julia language. This paper presents an implementation of two common model reduction methods: the Guyan reduction and the Craig-Bampton method. The goal was to implement these algorithms to the JuliaFEM platform and demonstrate that the code works correctly. This paper first describes the JuliaFEM concept briefly after which it presents the theory of model reduction, and finally, it demonstrates the implemented functions in an example model. This paper includes instructions for using the implemented algorithms, and reference the code itself in GitHub. The reduced stiness and mass matrices give the same results in both static and dynamic analyses as the original matrices, which proves that the code works correctly. The code runs smoothly on relatively large model of 12.6 million degrees of freedom. In future, damping could be included in the dynamic condensation now that it has been shown to work.

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