A sparse grid based collocation method for model order reduction of finite element approximations of passive electromagnetic devices under uncertainty

Sumant, Prasad S.; Wu, Hangbin; Cangellaris, A.C.; Aluru, N. R.

doi:10.1109/mwsym.2010.5517633

Cited by 14 publications

(13 citation statements)

References 6 publications

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“…where Z is the number of state-vector unknowns and depends on the particular EM method used to compute (10) and (11), and N p represent the number of ports of the system. In some recent contributions [24]- [26], it is proven that is possible to calculate efficiently the PC expansion of the system starting from the PC expansion of the corresponding model (state-space models in [24] and transmission line models in [25], [26]).…”

Section: Stochastic Model Order Reductionmentioning

confidence: 99%

“…In some recent contributions [24]- [26], it is proven that is possible to calculate efficiently the PC expansion of the system starting from the PC expansion of the corresponding model (state-space models in [24] and transmission line models in [25], [26]). Theoretically, a similar approach could be used for systems described by equations (10) and (11). Indeed, using the PC expansion (9) to express the state-space matrices, the state-vector and the output in equations (10) and (11) yields…”

Section: Stochastic Model Order Reductionmentioning

confidence: 99%

“…Indeed, the MOR techniques allow to reduce the complexity of large scale models and, therefore, the computational cost of the simulations [14]- [16]. Recently, the techniques [11]- [13] propose a more general PC-based method for the VA of large scale systems described by Helmholtz equations. The recently proposed methods described in [11]- [13] are based on the following steps:…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the techniques [11]- [13] propose a more general PC-based method for the VA of large scale systems described by Helmholtz equations. The recently proposed methods described in [11]- [13] are based on the following steps:…”

Section: Introductionmentioning

confidence: 99%

“…Despite their accuracy and efficiency with respect to the MCbased methods, the techniques [11]- [13] can be expensive both in terms of memory and computational time, since they require the calculation of a PC-based model of the original large scale equations and of the projection matrices.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Variability Analysis of Electromagnetic Systems Via Polynomial Chaos and Model Order Reduction

Spina

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Antonini

et al. 2014

IEEE Trans. Compon., Packag. Manufact. Technol.

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Abstract-We present a novel technique to perform the model order reduction of multiport systems under the effect of statistical variability of geometrical or electrical parameters. The proposed approach combines a deterministic model order reduction phase with the use of the Polynomial Chaos expansion to perform the variability analysis of the system under study very efficiently. The combination of model order reduction and Polynomial Chaos techniques generates a final reduced-order model able to accurately perform stochastic computations and variability analysis. The novel proposed method guarantees a high-degree of flexibility, since different model order reduction schemes can be used and different types of modern electrical systems (e.g. filters, connectors) can be modeled. The accuracy and efficiency of the proposed approach is verified by means of two numerical examples and compared with other existing variability analysis techniques.

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Section: Stochastic Model Order Reductionmentioning

confidence: 99%

Section: Stochastic Model Order Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Variability Analysis of Electromagnetic Systems Via Polynomial Chaos and Model Order Reduction

Spina

Ferranti

Antonini

et al. 2014

IEEE Trans. Compon., Packag. Manufact. Technol.

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Physics-Based Surrogate Modeling

Kozieł

Pietrenko‐Dabrowska

2020

Performance-Driven Surrogate Modeling of High-Frequency Structures

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Physics-Based Surrogate Modeling Using Response Correction

Kozieł

Leifsson

2016

Simulation-Driven Design by Knowledge-Based Response Correction Techniques

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The surrogate modeling and response correction techniques considered in this book were mostly discussed in the context of design optimization. In such a setup, the primary purpose of the surrogate is to ensure good local alignment with the highfidelity model, whereas global accuracy of the model is not of a major concern. In a more general setting, i.e., global or quasi-global modeling, the surrogate is to be valid within a larger portion of the design space. This is important for creating multiple-use library models and applications such as statistical analysis, uncertainty quantification, or global optimization. This chapter describes approaches to quasiglobal surrogate modeling using physics-based surrogates and response correction techniques. Formulation of the modeling problem is followed by a discussion of global modeling using space mapping, and space mapping enhanced by function approximation layers, as well as surrogate modeling with the shape-preserving response prediction. Finally, feature-based modeling for statistical design is described. The chapter ends with summary and discussion.

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A sparse grid based collocation method for model order reduction of finite element approximations of passive electromagnetic devices under uncertainty

Cited by 14 publications

References 6 publications

Efficient Variability Analysis of Electromagnetic Systems Via Polynomial Chaos and Model Order Reduction

Efficient Variability Analysis of Electromagnetic Systems Via Polynomial Chaos and Model Order Reduction

Physics-Based Surrogate Modeling

Physics-Based Surrogate Modeling Using Response Correction

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