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EnglishThis paper presents a preliminary version of a probabilistic model for the uncertainty quantification of complex electronic systems resulting from the combination of the least-squares 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.
Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by means of a Machine Learning Model Coupled with Kriging Regression / Trinchero, R.; Larbi, M.; Swaminathan, M.; Canavero, F. G.. - ELETTRONICO. - (2019), pp. 1-4. ((Intervento presentato al convegno 23rd IEEE Workshop on Signal and Power Integrity, SPI 2019 tenutosi a Chambéry, France, France nel 18-21 June 2019.
| Titolo: | Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by means of a Machine Learning Model Coupled with Kriging Regression |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | This paper presents a preliminary version of a probabilistic model for the uncertainty quantifica...tion of complex electronic systems resulting from the combination of the least-squares 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. |
| ISBN: | 978-1-5386-8342-2 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2768138
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