A Novel Method for Intrusive Stochastic Estimation of Geometric Tolerance Effects in Finite Element Electromagnetic Analysis

Narendranath, Abhijith B.; Vinoy, K. J.

doi:10.1109/tmtt.2021.3094211

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…Both intrusive and non‐intrusive numerical stochastic algorithms exist for handling uncertainties 2 . Intrusive uncertainty quantification (UQ) algorithms such as spectral stochastic finite element method (SSFEM), 3,4 and Neumann expansion (NE) 5 have been proposed in EM applications, where the parameter variations are modeled as a random variable. Such uncertainty models fail to capture spatial variations.…”

Section: Introductionmentioning

confidence: 99%

Modeling of spatial permittivity variations using Karhunen–Loève expansion for stochastic electromagnetic problems

Gladwin

Vinoy

2022

Int J RF Mic Comp-Aid Eng

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Fabrication tolerance often causes spatial variations in material properties in electromagnetic systems. In this paper, these spatial variations are modeled using Karhunen-Loéve expansion, which is truncated for a finite dimensional representation. Uncertainty quantification is performed by incorporating this model into an intrusive polynomial chaos expansion based spectral stochastic finite element method. Using a numerical example, it has been shown that the stochastic response obtained by the proposed approach is accurate and computationally efficient. The impact of correlation length of the covariance function used in the spectral expansion is analyzed.

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Section: Introductionmentioning

confidence: 99%

Modeling of spatial permittivity variations using Karhunen–Loève expansion for stochastic electromagnetic problems

Gladwin

Vinoy

2022

Int J RF Mic Comp-Aid Eng

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“…A typical example are the methods based on the framework of generalized polynomial chaos expansion (PCE) [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], which expand the stochastic quantities of interest in terms of orthogonal basis functions of the uncertain parameters. The number of basis functions grows exponentially with the input dimensionality and/or the expansion order, thus becoming intractable for problems with several parameters that require high-order expansions.…”

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confidence: 99%

Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

Manfredi

2023

IEEE Trans. Microwave Theory Techn.

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In this article, a probabilistic machine learning framework based on Gaussian process regression (GPR) and principal component analysis (PCA) is proposed for the uncertainty quantification (UQ) of microwave circuits. As opposed to most surrogate modeling techniques, GPR models inherently carry information on the model prediction uncertainty due to unseen data. This article shows how the inherent uncertainty of GPR pointwise predictions can be combined with the uncertainty of the design parameters to provide global statistical information on the device performance with the inclusion of confidence bounds. The model confidence is possibly improved by increasing the amount of training data. In addition, PCA is employed to effectively deal with problems with multiple and possibly complex-valued output components, such as those involving the UQ of time-domain responses or multiport scattering parameters. The proposed technique is successfully applied to two low-noise amplifier designs subject to the process variation of up to 25 parameters. Comparisons against the state-of-the-art polynomial chaos expansion method demonstrates that GPR achieves superior accuracy, while additionally providing information on the prediction confidence.

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A Nonintrusive POD-PCE Scheme for Uncertainty Quantification of Stochastic EM Problems

Hossain

Gladwin

Narendranath

2023

IEEE Microw. Wireless Tech. Lett.

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A Novel Method for Intrusive Stochastic Estimation of Geometric Tolerance Effects in Finite Element Electromagnetic Analysis

Cited by 8 publications

References 36 publications

Modeling of spatial permittivity variations using Karhunen–Loève expansion for stochastic electromagnetic problems

Modeling of spatial permittivity variations using Karhunen–Loève expansion for stochastic electromagnetic problems

Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

A Nonintrusive POD-PCE Scheme for Uncertainty Quantification of Stochastic EM Problems

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