Scalable models of microwave system responses using sequential sampling on unstructured grids

Chemmangat, Krishnan; Ferranti, Francesco; Dhaene, Tom; Knockaert, Luc

doi:10.1002/jnm.1903

Cited by 10 publications

(57 citation statements)

References 31 publications

(99 reference statements)

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“…An efficient sampling algorithm [17] is used to gather data samples located at maximally informative design space positions, i.e. spots in the design space where the response changes rapidly, and then to minimize as much as possible the amount of data samples needed to build and validate a scalable macromodel.…”

Section: Building a Scalable Macromodel From Data Samples Generated Bmentioning

confidence: 99%

“…Otherwise, the region is split into two subregions along the maximum sensitive edge [17]. The accuracy threshold ∆ can be decided based on the design specifications set by the user.…”

Section: Refinementmentioning

confidence: 99%

“…Even though the extraction process of scalable macromodels can be automated using sequential sampling approaches [17], it still requires the designer to specify additional information. For example, the designer must select ranges for the design parameters, for which the model is built.…”

Section: Introductionmentioning

confidence: 99%

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Scalable macromodelling methodology for the efficient design of microwave filters

Caenepeel

Chemmangat

Ferranti

et al. 2016

IET Microwaves, Antennas & Propagation

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The complexity of the design of state-of-the art microwave filters increases steadily over the years. General design techniques available in literature yield relatively good initial designs, but electromagnetic (EM) optimization is often needed to meet the desired specifications. Although interesting optimization strategies exist, they depend on computationally expensive EM simulations. This makes the optimization process time consuming. Moreover, brute force optimization does not provide physical insights in the design and it is only applicable to one set of specifications. If the specifications change, the design and optimization process must be redone. We propose to use a scalable macromodel-based design approach to overcome this. Scalable macromodels can be generated in an efficient and automated way.So far the inclusion of scalable macromodels in the design cycle of microwave filters 1 has not been studied and discussed. In this paper, we show that scalable macromodels can be included in the design cycle of microwave filters and re-used in multiple design scenarios at a low computational cost. We give guidelines to properly generate and use scalable macromodels in a filter design context. We illustrate the approach on a stateof-the-art design example: a microstrip dual-band bandpass filter with closely spaced pass bands and a complex geometrical structure. The results confirm that scalable macromodels are proper design tools and an efficient and accurate alternative to a computationally expensive EM simulator-based design flow.

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Section: Building a Scalable Macromodel From Data Samples Generated Bmentioning

confidence: 99%

“…Otherwise, the region is split into two subregions along the maximum sensitive edge [17]. The accuracy threshold ∆ can be decided based on the design specifications set by the user.…”

Section: Refinementmentioning

confidence: 99%

See 1 more Smart Citation

Scalable macromodelling methodology for the efficient design of microwave filters

Caenepeel

Chemmangat

Ferranti

et al. 2016

IET Microwaves, Antennas & Propagation

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“…Also, for relatively high dimensions the memory requirement for these methods can be relatively high since big matrices has to be solved, limiting their applicability [7][8][9]12]. Recently a local tree-based sequential sampling has been proposed in [14] which uses interpolation-based local scalable macromodeling method to build accurate parameterized macromodels. The method is able to preserve system properties and can also build multi-fidelity models.…”

Section: Introductionmentioning

confidence: 99%

“…Also the method is implemented as a tree with independent branches for different regions of the design space making it easily expandable and portable to parallel computing platforms. In this paper, we improve the method of [14], which we refer throughout this paper as grid method, on the following aspects:…”

Section: Introductionmentioning

confidence: 99%

Auto-generation of passive scalable macromodels for microwave components using scattered sequential sampling

Chemmangat

Dhaene

Knockaert

2014

Int. J. Microw. Wireless Technol.

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A hybrid adaptive sampling algorithm for obtaining reduced order models for systems with frequency dependent state‐space matrices

Samuel

Ferranti

Knockaert

et al. 2015

Int J Numerical Modelling

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This paper proposes a hybrid adaptive sampling algorithm to automate the generation of reduced order models for systems described by large-scale frequency dependent state-space models. The evaluation of the frequency dependent state-space model for each frequency sample can be computationally expensive. The distribution of frequency samples must be optimized to avoid oversampling and undersampling. In order to have an optimum number of frequency samples, the proposed algorithm uses the reflective exploration technique for the adaptive selection of samples, and the sampling is further refined using a binary search to validate the frequency dependent reduced order models. Projection-based model order reduction techniques are used for obtaining the reduced order model. The projection matrix for each frequency sample is merged to obtain a common projection matrix for all samples. However, in certain cases when the number of sample points increases, the merged projection matrix also increases in dimension and might fail to provide a satisfactory reduction in model size. Thus, the merged projection matrix is truncated based on its singular values to obtain a compact common projection matrix. Then, the reduced order state-space matrices per frequency are interpolated over the frequency range of interest to obtain the system response. Pertinent examples validate the proposed hybrid adaptive sampling algorithm. Copyright (C) 2015 John Wiley & Sons, Ltd

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Scalable models of microwave system responses using sequential sampling on unstructured grids

Cited by 10 publications

References 31 publications

Scalable macromodelling methodology for the efficient design of microwave filters

Scalable macromodelling methodology for the efficient design of microwave filters

Auto-generation of passive scalable macromodels for microwave components using scattered sequential sampling

A hybrid adaptive sampling algorithm for obtaining reduced order models for systems with frequency dependent state‐space matrices

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