Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters

Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters

This paper deals with the application of the support vector machine (SVM) and the least-squares SVM regressions to the uncertainty quantification of complex systems with a high-dimensional parameter space. The above regression techniques are used to build accurate and compact surrogate models of the...

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Bibliographic Details
Main Authors: Riccardo Trinchero, Mourad Larbi, Hakki M. Torun, Flavio G. Canavero, Madhavan Swaminathan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Machine learning
uncertainty quantification
parameterized modeling
surrogate models
SVM regression
LS-SVM regression
Online Access:https://ieeexplore.ieee.org/document/8584446/

Internet

https://ieeexplore.ieee.org/document/8584446/

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