Articlegold2018

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

Riccardo Trinchero · Mourad Larbi · Hakki Mert Torun · Flavio Canavero · Madhavan Swaminathan

110 citations0/0 files storedIEEE Access

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Probabilistic and Robust Engineering Design Model Reduction and Neural Networks Gaussian Processes and Bayesian Inference

Abstract

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.

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References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code 2007Global sensitivity analysis using polynomial chaos expansions 2010Adaptive sparse polynomial chaos expansion based on least angle regression 2005Design and Analysis of Experiments 2004Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure 2006Stochastic finite element: a non intrusive approach by regression 2013Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos

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2022Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes 2021A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression 2020A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses 2025A hybrid polynomial chaos expansion – Gaussian process regression method for Bayesian uncertainty quantification and sensitivity analysis 2024Nonparametric formulation of polynomial chaos expansion based on least-square support-vector machines

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