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EnglishThis paper provides a quick overview on three machine learning regression techniques for the uncertainty quantification and the parametric modeling of the responses of electronic systems. Specifically, in this work support vector machine, least-squares support vector machine and Gaussian process regressions are adopted to build accurate and fast-to-evaluate metamodels for the prediction of the behaviour of the output of interest in stochastic systems as a function of the uncertain parameters. The above regressions techniques are trained from a limited set of training pairs provided by either measurements or simulations of the full-computational model. The resulting metamodels can be suitably adopted for both uncertainty quantification and optimization purposes, thus providing the user with a set of helpful tools for the design of complex electrical systems. The feasibility and the accuracy of the considered machine learning regression techniques are investigated by considering a realistic printed circuit board interconnect structure.
Machine Learning for the Design of a Distribution Network for High-Speed Signals / Trinchero, R.; Canavero, F. G.. - ELETTRONICO. - (2019), pp. 1038-1041. ((Intervento presentato al convegno 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA) tenutosi a Granada, Spain, Spain nel 9-13 Sept. 2019.
| Titolo: | Machine Learning for the Design of a Distribution Network for High-Speed Signals | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | This paper provides a quick overview on three machine learning regression techniques for the unce...rtainty quantification and the parametric modeling of the responses of electronic systems. Specifically, in this work support vector machine, least-squares support vector machine and Gaussian process regressions are adopted to build accurate and fast-to-evaluate metamodels for the prediction of the behaviour of the output of interest in stochastic systems as a function of the uncertain parameters. The above regressions techniques are trained from a limited set of training pairs provided by either measurements or simulations of the full-computational model. The resulting metamodels can be suitably adopted for both uncertainty quantification and optimization purposes, thus providing the user with a set of helpful tools for the design of complex electrical systems. The feasibility and the accuracy of the considered machine learning regression techniques are investigated by considering a realistic printed circuit board interconnect structure. | |
| ISBN: | 978-1-7281-0563-5 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| PID5950099.pdf | submitted paper | 1. Preprint / Submitted Version | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2768137
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