Power Electronics Modeling and Design: Using Parametric and Model-Form Uncertainty Quantification to Assess Predictive Accuracy of Power Converter Models

Mehrabadi, Niloofar Rashidi; Burgos, Rolando; Roy, Christopher J.; Boroyevich, Dushan

doi:10.1109/mpel.2017.2760250

Cited by 10 publications

(2 citation statements)

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“…at the end of each LARS iteration. c) Compare the sample variance in (12) with the variance calculated at each LARS iteration in (13).…”

Section: ) Methods To Improve the Lars Methodmentioning

confidence: 99%

“…In the numerical simulations, the Debye parameters are considered as the input parameters of the system used to determine the electromagnetic response of the human body. The uncertainty of these input parameters can be described by their probabilistic description [12]. However, uncertainty also exists in the system response, due to the propagation of the randomness of the input parameters.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Least Angle Regression Method for Uncertainty Quantification in FDTD Computation

Monebhurrun

Himeno

et al. 2018

IEEE Trans. Antennas Propagat.

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The non-intrusive polynomial chaos (NIPC) expansion method is used to quantify the uncertainty of a stochastic system. It potentially reduces the number of numerical simulations in modelling process, thus improving efficiency, whilst ensuring accuracy. However, the number of polynomial bases grows substantially with the increase of random parameters, which may render the technique ineffective due to the excessive computational resources. To address such problems, methods based on the sparse strategy such as the least angle regression (LARS) method with hyperbolic index sets can be used. This paper presents the first work to improve the accuracy of the original LARS method for uncertainty quantification (UQ). We propose an adaptive LARS method in order to quantify the uncertainty of the results from the numerical simulations with higher accuracy than the original LARS method. The proposed method outperforms the original LARS method in terms of accuracy and stability. The L2 regularisation scheme further reduces the number of input samples while maintaining the accuracy of the LARS method.Index Terms-Non-intrusive polynomial chaos (NIPC) expansion, least angle regression (LARS), uncertainty quantification (UQ), finite difference time domain (FDTD), Debye media

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“…at the end of each LARS iteration. c) Compare the sample variance in (12) with the variance calculated at each LARS iteration in (13).…”

Section: ) Methods To Improve the Lars Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%