Adaptive Sampling Algorithm for Macromodeling of Parameterized $S$-Parameter Responses

Deschrijver, Dirk; Crombecq, Karel; Nguyen, Huu Minh; Dhaene, Tom

doi:10.1109/tmtt.2010.2090407

Cited by 59 publications

(37 citation statements)

References 20 publications

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“…It does not require intermediate models to make sampling decisions and has been applied to multiple real-world test cases from different problem domains by users of the SUMO Toolbox in several studies [1,10,28,23,2,11,27]. The performance of this method comes at the cost of computational complexity, which grows rapidly as the dimensionality of the problem increases.…”

Section: Discussionmentioning

confidence: 99%

“…In previous studies [1,10,28,23,2,11,27] in several research fields, LOLA-Voronoi has proven to be an excellent algorithm to building sequential designs. The sampling distribution is modified to focus on non-linear regions at the expense of a small computational cost for low-dimensional problems.…”

Section: Methodsmentioning

confidence: 99%

“…The LOLA-Voronoi algorithm [8,7], an earlier proposed hybrid iterative scheme that distributes the points to cover the design space and distributes the data density proportional to the non-linearity of f has proven to be very useful in several studies in several fields: [1,10,28,23,2,11,27]. Non-linear regions are more difficult to model, so the additional data points in these regions greatly help the search for a good approximationf .…”

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confidence: 99%

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A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments

Herten¹,

Couckuyt²,

Deschrijver³

et al. 2015

SIAM J. Sci. Comput.

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Abstract. Complex real-world systems can accurately be modeled by simulations. Evaluating high-fidelity simulators can take several days, making them impractical for use in optimization, design space exploration and analysis. Often, these simulators are approximated by relatively simple math known as a surrogate model. The data points to construct this model are simulator evaluations meaning the choice of these points is crucial: each additional data point can be very expensive in terms of computing time. Sequential design strategies offer a huge advantage over one-shot experimental design because information gathered from previous data points can be used in the process of determining new data points. Previously, LOLA-Voronoi was presented as a hybrid sequential design method which balances exploration and exploitation: the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in interesting regions which were previously discovered. Although this approach is very successful in terms of the required number of data points to build an accurate surrogate model, it is computationally intensive. This paper presents a new approach to the exploitation component of the algorithm based on Fuzzy logic. The new approach has the same desirable properties of the old method but is less complex, especially when applied to high-dimensional problems. Experiments on several test problems show the new approach is a lot faster, without losing robustness or requiring additional samples to obtain similar model accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments

Herten¹,

Couckuyt²,

Deschrijver³

et al. 2015

SIAM J. Sci. Comput.

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“…In successive steps, additional sampling points are selected one by one in a sequential way until the overall variation of the NF pattern is characterized. In order to sample the NF pattern as efficiently as possible, the robust sampling strategy from [10]- [12] is applied to determine optimal coordinates of the sampling points in a sequential way [13]- [15]. The sampling algorithm makes a balanced trade-off between exploration and exploitation criteria: • Exploitation ensures that additional scans are performed in regions of the design space where the amplitude of the near-field component that is being measured is changing more rapidly.…”

Section: Sequential Sampling Algorithm For Two Simultaneous Modelsmentioning

confidence: 99%

Sequential sampling algorithm for simultaneous near-field scanning of amplitude and phase

Claeys

Pissoort

Deschrijver

et al. 2014

2014 International Symposium on Electromagnetic Compatibility

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Abstract-This paper describes an automated sequential sampling algorithm for EMI near-field scanning of electronic systems which allows to measure both magnitude and phase of the electromagnetic near-fields simultaneously. The main goal of the sequential sampling algorithm is to drastically reduce the total measurement time to obtain a complete model of the electronic system's near-field distribution. Measuring both magnitude and phase is important for predicting the far-field emission from the near-field or for building equivalent radiation models of the device under test. Previous work described such a sequential sampling algorithm for amplitude-only measurements. The extension towards both amplitude and phase poses two challenges. First, the underlying sampling and modelling techniques have to be adapted such that they can handle building up two separate models at the same time using a common set of optimal sampling points and without significant increase of the measurement time. Second, a good choice has to be made with respect to which components will be sampled and modelled. It is shown that the most advantageous choice is to sample and model the real and imaginary components of the near-fields instead of the amplitude and phase directly.

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“…In order to reduce the computational workload, a new sequential sampling strategy [11] is modified such that data samples are scattered in terms of all parameters, including angular frequency [12]. It automatically selects a suitable sample distribution, which accurately captures the dynamical behavior of the system.…”

Section: Introductionmentioning

confidence: 99%

Sequential sampling strategy for the modeling of parameterized microwave and RF components

Deschrijver

Crombecq

Nguyen

et al. 2011

2011 IEEE 20th Conference on Electrical Performance of Electronic Packaging and Systems

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Abstract-Accurate modeling of parameterized microwave and RF components often requires a large number of full-wave electromagnetic simulations. In order to reduce the overall simulation cost, a sequential sampling algorithm is proposed that selects a sparse set of data samples which characterize the overall response of the system. The resulting data samples can be fed into existing modeling techniques. The effectiveness of the approach is illustrated by a parameterized H-shaped microwave antenna.

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Adaptive Sampling Algorithm for Macromodeling of Parameterized $S$-Parameter Responses

Cited by 59 publications

References 20 publications

A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments

A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments

Sequential sampling algorithm for simultaneous near-field scanning of amplitude and phase

Sequential sampling strategy for the modeling of parameterized microwave and RF components

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