This paper presents a preliminary version of an Active Learning (AL) scheme for the sample selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian Process regression. The proposed AL strategy iteratively searches for new candidate points to be included within the training set by trying to minimize the relative posterior standard deviation provided by the Gaussian Process regression surrogate. The above scheme has been applied for the construction of a surrogate model for the statistical analysis of the efficiency of a switching buck converter as a function of 7 uncertain parameters. The performance of the surrogate model constructed via the proposed active learning method are compared with the ones provided by an equivalent model built via a latin hypercube sampling. The results of a Monte Carlo simulation with the computational model are used as reference.

Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices / Trinchero, Riccardo; Canavero, Flavio. - In: ENGINEERING PROCEEDINGS. - ISSN 2673-4591. - ELETTRONICO. - 3:(2020), p. 3. ((Intervento presentato al convegno 1st International Electronic Conference - Futuristic Applications on Electronics session nel 01/11/2020 - 30/11/2020 [10.3390/iec2020-06967].

Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices

Riccardo Trinchero;Flavio Canavero
2020

Abstract

This paper presents a preliminary version of an Active Learning (AL) scheme for the sample selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian Process regression. The proposed AL strategy iteratively searches for new candidate points to be included within the training set by trying to minimize the relative posterior standard deviation provided by the Gaussian Process regression surrogate. The above scheme has been applied for the construction of a surrogate model for the statistical analysis of the efficiency of a switching buck converter as a function of 7 uncertain parameters. The performance of the surrogate model constructed via the proposed active learning method are compared with the ones provided by an equivalent model built via a latin hypercube sampling. The results of a Monte Carlo simulation with the computational model are used as reference.
File in questo prodotto:
File Dimensione Formato  
Trinchero_IEC2020_GP+AL_approved.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 565.44 kB
Formato Adobe PDF
565.44 kB Adobe PDF Visualizza/Apri
manuscript.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 580.09 kB
Formato Adobe PDF
580.09 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2863732