@inproceedings{7164aa51058840b0a0f955fc5c6f056a,
title = "Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by means of a Machine Learning Model Coupled with Kriging Regression",
abstract = "This paper presents a preliminary version of a probabilistic model for the uncertainty quantification of complex electronic systems resulting from the combination of the leastsquares support vector machine (LS-SVM) and the Gaussian process (GP) regression. The proposed model, trained with a limited set of training pairs provided by a set of full-wave expensive simulations, is adopted for the prediction of the efficiency of an integrated voltage regulator (IVR) with 8 uniformly distributed random parameters. The accuracy and the feasibility of the proposed model have been investigated by comparing the model predictions and its confidence intervals with the results of a Monte Carlo (MC) full-wave simulation of the device.",
author = "R. Trinchero and M. Larbi and M. Swaminathan and Canavero, {F. G.}",
note = "Funding Information: The Authors are grateful to Mr. Hakki M. Torun, Ph.D student at School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA, for providing the IVR simulation data. Publisher Copyright: {\textcopyright} 2019 IEEE.; 23rd IEEE Workshop on Signal and Power Integrity, SPI 2019 ; Conference date: 18-06-2019 Through 21-06-2019",
year = "2019",
month = jun,
doi = "10.1109/SaPIW.2019.8781659",
language = "English (US)",
series = "2019 IEEE 23rd Workshop on Signal and Power Integrity, SPI 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 23rd Workshop on Signal and Power Integrity, SPI 2019 - Proceedings",
address = "United States",
}