Comparison of Model Reduction Methods with Applications to Circuit Simulation

Ionutiu, R Roxana; Lefteriu, Sanda; Antoulas, Athanasios C.

doi:10.1007/978-3-540-71980-9_1

Cited by 14 publications

(17 citation statements)

References 8 publications

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“…However, these methods are not passivity preserving in general. A comparison of different modelreduction methods applicable to systems with singular E can be found in [24]. The dominant spectral-zero interpolation method discussed here successfully handles descriptor systems while preserving passivity and stability irrespective of how the system equations are formulated and can be implemented efficiently.…”

Section: A Model-reduction Problemmentioning

confidence: 98%

“…For these methods, convergence starts with well-separated eigenmodes located at the extremes of the spectrum. However, extreme eigenmodes are not necessarily dominant according to measure (21). Furthermore, dominance criteria other than (21) could be relevant, depending on the application.…”

Section: Iterative Algorithm For Dominant Spectral-zero Approximatmentioning

confidence: 98%

“…This diminishes performance of SADPA (particularly in the solve step 3), see Appendix B, Algorithm 1). We avoid this by replac- (12) and (13), with the equivalent structured Hamiltonian system Σ s (E s , A s , B s , C s , Δ) in the form of (24) and (25), which preserves sparsity. This is demonstrated through the following theorem.…”

Section: A Structured Hamiltonianmentioning

confidence: 99%

“…Model reduction thus becomes numerically more sensitive and constructing the required projections is more involved than for the case when E is invertible, particularly for balancing-type methods [5], [18] or for partial realization problems [20]. If E is invertible, system (1) can be converted to state-space representation through left multiplication by E −1 (see [2] for an overview of model-reduction methods for statespace systems; a study on different model-reduction methods for systems with invertible E can be found in [21]). …”

Section: A Model-reduction Problemmentioning

confidence: 99%

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Passivity-Preserving Model Reduction Using Dominant Spectral-Zero Interpolation

Ionutiu

Rommes

Antoulas

2008

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In this paper, the dominant spectral-zero method (dominant SZM) is presented, a new passivity-preserving modelreduction method for circuit simulation. Passivity is guaranteed via spectral-zero interpolation, and a dominance criterion is proposed for selecting spectral zeros. Dominant SZM is implemented as an iterative eigenvalue-approximation problem using the subspace-accelerated dominant-pole algorithm. Passive circuits are reduced automatically irrespective of how the original system equations are formulated (e.g., circuit models containing controlled sources or susceptance elements). Dominant SZM gives comparable and often more accurate reduced models than known techniques such as PRIMA, modal approximation, or positive real balanced truncation.

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Section: A Model-reduction Problemmentioning

confidence: 98%

Section: Iterative Algorithm For Dominant Spectral-zero Approximatmentioning

confidence: 98%

Section: A Structured Hamiltonianmentioning

confidence: 99%

Section: A Model-reduction Problemmentioning

confidence: 99%

See 2 more Smart Citations

Passivity-Preserving Model Reduction Using Dominant Spectral-Zero Interpolation

Ionutiu

Rommes

Antoulas

2008

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Since the resulting networks are too large to be handled efficiently with circuit simulators, model order reduction (MOR) techniques (Antoulas, 2005;Gugercin and Antoulas, 2004;Phillips and Silveira, 2005) can be used to reduce the size of the network while preserving the behaviour at selected nodes (Silveira, 1995;Freund, 2008;Ionutiu et al, 2007). The large number of independent sources of the networks is a limitation for the reduction, as only the RLC-part can be reduced, and the independent sources have to be extracted and connected by ports.…”

Section: Introductionmentioning

confidence: 99%

Model reduction of parasitic coupling networks of mixed‐signal VLSI circuits

Ludwig

Mathis

2011

COMPEL

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Purpose -This paper aims to present a method for the efficient reduction of networks modelling parasitic couplings in very-large-scale integration (VLSI) circuits. Design/methodology/approach -The parasitic effects are modelled by large RLC networks and current sources for the digital switching currents. Based on the determined behaviour of the digital modules, an efficient description of these networks is proposed, which allows for a more efficient model reduction than standard methods. Findings -The proposed method enables a fast and efficient simulation of the parasitic effects. Additionally, an extension of the reduction method to elements, which incorporate some supply voltage dependence to model the internal currents more precisely than independent current sources is presented. Practical implications -The presented method can be applied to large electrical networks, used in the modelling of parasitic effects, for reducing their size. A reduced model is created which can be used in investigations with circuit simulators requiring a lowered computational effort. Originality/value -Contrary to existing methods, the presented method includes the knowledge of the behaviour of the sources in the model to enhance the model reduction process.

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Challenges in Model Reduction for Real-Time Simulation of Traction Chain Systems

Ionutiu

2022

Realization and Model Reduction of Dynamical Systems

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Comparison of Model Reduction Methods with Applications to Circuit Simulation

Cited by 14 publications

References 8 publications

Passivity-Preserving Model Reduction Using Dominant Spectral-Zero Interpolation

Passivity-Preserving Model Reduction Using Dominant Spectral-Zero Interpolation

Model reduction of parasitic coupling networks of mixed‐signal VLSI circuits

Challenges in Model Reduction for Real-Time Simulation of Traction Chain Systems

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