Cost-effective global surrogate modeling of planar microwave filters using multi-fidelity bayesian support vector regression

Jacobs, J. Pieter; Koziel, Slawomir

doi:10.1002/mmce.20707

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Different Algorithms mathematical models [12,[39][40][41][42] citations (17,5,40,5,35) measurement tools [43,44] citations (43,11) Different hyperparameters mesh size [45][46][47] citations (2,1,13) convergence condition and simulated objects [10] citations (1) iteration limit [48] citations (25)…”

Section: Mechanical Engineeringmentioning

confidence: 99%

“…In reference [28], the doublet-lattice method and CFD-based flutter calculation are used to obtain LF and HF data, respectively. In mechanical engineering, different mathematical models (e.g., different finite element models [39,42] and surrogate models [12,40,41]) or different measuring tools (e.g., different strain sensors [43] and different wind-turbine-specific aero-servo-elastic computer simulators [44]) are commonly used to obtain MF data. For other fields, mainly in biomedical and economic fields, the methods of acquiring multi-fidelity data are listed in Table 1.…”

Section: Multi-fidelity Data From Different Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

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Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data.

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Section: Mechanical Engineeringmentioning

confidence: 99%

Section: Multi-fidelity Data From Different Algorithmsmentioning

confidence: 99%

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Wang,

Liu,

Chen

et al. 2023

Applied Sciences

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“…Whereas the extraction of the coupling matrix is difficult when the detuning is high. In addition, Bayesian SVR have been applied to the modeling of plannar antennas and microwave filters, which aims to reduce training sets and establishes a high‐fidelity BSVR model . Reference presents a hybrid modeling approach to solve problem of the deficiency of measured data and improve modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Cao

Liu

Yuan

et al. 2018

Int J RF Microw Comput Aided Eng

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In this article, an advanced technique is developed to combine multi‐output least‐squares support vector regression (MLS‐SVR) and pole‐residue‐based transfer function models for microwave filter parametric modeling. MLS‐SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS‐SVR is an effective method to cope with the multi‐output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi‐output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y‐parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial‐resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method.

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“…Utilizing the measured dataset between the positions of the tuning screws and the electrical parameters of equivalent circuits, Zhou utilized the least square SVR to develop a mechanism model for an automatic tuning robot of microwave filters [12]. In addition, Bayesian SVR has been applied to modeling applications of planar antennas and microwave filters, and the coarse-discretization electromagnetic simulations were used to find a reduced number of fine-discretization training points for establishing a high-fidelity model [13][14][15]. All of these algorithms have required sufficient data samples to establish an accurate model that describes the underlying nonlinear input-output mechanism.…”

Section: Introductionmentioning

confidence: 99%

Hybrid modeling of microwave devices using multi-kernel support vector regression with prior knowledge

Zhou

Huang

2014

Int J RF and Microwave Comp Aid Eng

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This article proposes a support-vector hybrid modeling method of microwave devices when only a small number of measurements are available. In this method, a hybrid model of microwave device has been obtained by combining a coarse model and a supportvector model, where the coarse model is complemented by a support-vector model capable of correcting the difference between the measurements and the coarse model. The supportvector model was developed using a novel algorithm. In the algorithm, multi-kernel and prior knowledge from a calibrated simulator were incorporated into the framework of the linear programming support vector regression by utilizing multiple feature spaces and modifying the optimization formulation. The experimental results from two microwave devices show that the hybrid modeling can enhance the physical meaning of the support-vector model and improve the modeling accuracy for a small dataset, and that the proposed algorithm shows great potential in some applications where sufficient experimental data is difficult and costly to obtain, but the prior knowledge from a simulation model is available. The hybrid modeling is suited to a microwave computer-aided design tool or an automatic tuning robot.

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Cost-effective global surrogate modeling of planar microwave filters using multi-fidelity bayesian support vector regression

Cited by 7 publications

References 14 publications

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies

Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Hybrid modeling of microwave devices using multi-kernel support vector regression with prior knowledge

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