Reduced Dimensional Chebyshev-Polynomial Chaos Approach for Fast Mixed Epistemic-Aleatory Uncertainty Quantification of Transmission Line Networks

Prasad, Aditi Krishna; Roy, Sourajeet

doi:10.1109/tcpmt.2018.2880995

Cited by 19 publications

(7 citation statements)

References 39 publications

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“…In particular, direct EM-based Monte Carlo analysis 18 typically requires many hundreds of system evaluations, which often turns prohibitive. Accelerated methods rely on simplifications (e.g., worst case analysis 19 ), fast replacement models (surrogates), e.g., neural networks 20 , polynomial chaos expansion 21 , 22 , dimensionality reduction (e.g., principal component analysis 23 , variable-fidelity simulations 24 or physics-based modeling 25 ).…”

Section: Introductionmentioning

confidence: 99%

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates

Koziel,

Haq

2023

Sci Rep

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Modern microwave devices are designed to fulfill stringent requirements pertaining to electrical performance, which requires, among others, a meticulous tuning of their geometry parameters. When moving up in frequency, physical dimensions of passive microwave circuits become smaller, making the system performance increasingly susceptible to manufacturing tolerances. In particular, inherent inaccuracy of fabrication processes affect the fundamental operating parameters, such as center frequency or bandwidth, which is especially troublesome for narrow-band structures, including notch filters. The ability to quantify the effects of tolerances, and—even more—to account for these in the design process, are instrumental in making the designs more reliable, and to increase the likelihood that adequate operation is ensured despite manufacturing errors. This paper proposes a simple yet computationally efficient and reliable procedure for statistical analysis and yield optimization of resonator-based notch filters. Our methodology involves feature-based surrogate models that can be established using a handful of training data points, and employed for rapid evaluation of the circuit fabrication yield. Furthermore, a yield optimization procedure is developed, which iteratively sets up a sequence of feature-based models, constructed within local domains relocated along the optimization path, and uses them as predictors to find a robust (maximum yield) design at a low computational cost. The presented approach has been demonstrated using two complementary split ring resonator (CSRR)-based notch filters. The cost of statistical design is about a hundred of EM simulations of the respective filter, with yield evaluation reliability corroborated through EM-based Monte Carlo analysis.

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Section: Introductionmentioning

confidence: 99%

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates

Koziel,

Haq

2023

Sci Rep

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“…Jiang et al [20] reviewed the probability-interval hybrid uncertainties from the perspective of uncertainty modeling, uncertainty propagation, structural reliability analysis and reliability-based design optimization. Prasad et al [21] conducted a Chebyshev-polynomial chaos metamodel for uncertainty analysis for mixed epistemic-aleatory problems caused by imprecise knowledge and random variability in the transmission of line networks. Yin et al [22] established a hybrid Finite Element/Statistical Energy Analysis model considering fuzzy and interval uncertainties based on first-order perturbation, second-order perturbation and Chebyshev approximation.…”

Section: Introductionmentioning

confidence: 99%

A distributed dynamic load identification method based on the hierarchical-clustering-oriented radial basis function framework using acceleration signals under convex-fuzzy hybrid uncertainties

Liu

Wang

et al. 2022

Mechanical Systems and Signal Processing

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“…Consequently, uncertainty quantification is of high practical importance, for both academia and industry. Among the two groups of uncertainties, the most commonly considered in antenna design are the aleatory ones 1 . These are primarily deviations of antenna geometry parameters from their nominal values, resulting from limited accuracy of the manufacturing and/or assembly procedures, and described by probability distributions 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

Low-cost yield-driven design of antenna structures using response-variability essential directions and parameter space reduction

Pietrenko‐Dabrowska

Kozieł

Golunski

2022

Sci Rep

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Quantifying the effects of fabrication tolerances and uncertainties of other types is fundamental to improve antenna design immunity to limited accuracy of manufacturing procedures and technological spread of material parameters. This is of paramount importance especially for antenna design in the industrial context. Degradation of electrical and field properties due to geometry parameter deviations often manifests itself as, e.g., center frequency shifts or compromised impedance matching. Improving antenna performance at the presence of uncertainties is typically realized through maximization of the fabrication yield. This is normally carried out at the accuracy level of full-wave electromagnetic (EM) analysis, which incurs considerable computational expenses. The involvement of surrogate modeling techniques is the most common approach to alleviating these difficulties, yet conventional modeling methods suffer to a great extent form the curse of dimensionality. This work proposes a technique for low-cost yield optimization of antenna structures. It capitalizes on meticulous definition of the domain of the metamodel constructed for statistical analysis purposes. The domain is spanned by a limited number of essential directions being the most influential in terms of affecting antenna responses in the frequency bands of interest. These directions are determined through an automated decision-making process based on the assessment of the circuit response variability. Our approach permits maintaining small domain volume, which translates into low cost of surrogate model setup, while providing sufficient room for yield improvement. The presented method is validated using three antenna structures and favorably compared to several surrogate-assisted benchmark methods. EM-driven Monte Carlo simulation is also conducted to verify reliability of the yield optimization process.

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Reduced Dimensional Chebyshev-Polynomial Chaos Approach for Fast Mixed Epistemic-Aleatory Uncertainty Quantification of Transmission Line Networks

Cited by 19 publications

References 39 publications

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates

A distributed dynamic load identification method based on the hierarchical-clustering-oriented radial basis function framework using acceleration signals under convex-fuzzy hybrid uncertainties

Low-cost yield-driven design of antenna structures using response-variability essential directions and parameter space reduction

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