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 system responses from a limited set of training samples. The accuracy and the feasibility of the proposed modeling techniques are then investigated by comparing their results with the ones predicted by a sparse polynomial chaos expansion by considering two real-life problems with 8 and 30 random variables, respectively.
|Titolo:||Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices with a Large Number of Parameters|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||10.1109/ACCESS.2018.2888903|
|Appare nelle tipologie:||1.1 Articolo in rivista|