This paper introduces a probabilistic nonparametric surrogate model based on Gaussian process regression to perform uncertainty quantification tasks with the inclusion of confidence bounds on the predicted statistics. The performance of the proposed method is compared against two state-of-the-art techniques, namely the parametric sparse polynomial chaos expansion and the nonparametric least-square support vector machine regression.
A Nonparametric Surrogate Model for Stochastic Crosstalk Analysis Including Confidence Bounds / Manfredi, Paolo; Trinchero, Riccardo. - ELETTRONICO. - (2021), pp. 1-4. ((Intervento presentato al convegno 2021 IEEE 25th Workshop on Signal and Power Integrity tenutosi a Siegen, Germany nel 10-12 May 2021 [10.1109/spi52361.2021.9505176].
Titolo: | A Nonparametric Surrogate Model for Stochastic Crosstalk Analysis Including Confidence Bounds | |
Autori: | ||
Data di pubblicazione: | 2021 | |
Abstract: | This paper introduces a probabilistic nonparametric surrogate model based on Gaussian process reg...ression to perform uncertainty quantification tasks with the inclusion of confidence bounds on the predicted statistics. The performance of the proposed method is compared against two state-of-the-art techniques, namely the parametric sparse polynomial chaos expansion and the nonparametric least-square support vector machine regression. | |
ISBN: | 978-1-6654-2388-5 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2921772