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

IEEE Access

Riccardo Trinchero¹  Flavio G. Canavero¹  Madhavan Swaminathan²  Hakki M. Torun²  Mourad Larbi² 

[1] Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy;School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA;

关键词: Machine learning;    uncertainty quantification;    parameterized modeling;    surrogate models;    SVM regression;    LS-SVM regression;   

DOI  :  10.1109/ACCESS.2018.2888903

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown