This paper discusses the application of a probabilistic surrogate modeling technique, based on Gaussian process regression (GPR), to the uncertainty quantification (UQ) of crosstalk. Compared to traditional deterministic surrogate models, the GPR provides a stochastic process that carries an estimate of the model uncertainty. This allows assigning confidence bounds to the model prediction and, in an UQ scenario, to statistical estimates. The advocated method is illustrated through its application to a literature test case.
Statistical crosstalk analysis via probabilistic machine learning surrogates / Manfredi, Paolo; Trinchero, Riccardo. - ELETTRONICO. - (2021), pp. 1-3. ((Intervento presentato al convegno IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS 2021) tenutosi a Austin, TX, USA nel 17-20 ottobre 2021 [10.1109/EPEPS51341.2021.9609229].
Titolo: | Statistical crosstalk analysis via probabilistic machine learning surrogates | |
Autori: | ||
Data di pubblicazione: | 2021 | |
Abstract: | This paper discusses the application of a probabilistic surrogate modeling technique, based on Ga...ussian process regression (GPR), to the uncertainty quantification (UQ) of crosstalk. Compared to traditional deterministic surrogate models, the GPR provides a stochastic process that carries an estimate of the model uncertainty. This allows assigning confidence bounds to the model prediction and, in an UQ scenario, to statistical estimates. The advocated method is illustrated through its application to a literature test case. | |
ISBN: | 978-1-6654-4269-5 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
File | Descrizione | Tipologia | Licenza | |
---|---|---|---|---|
manfredi-EPEPS-2021-final.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
cnf-2021-EPEPS.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2949650