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

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References

2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code2,856 cited
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2022Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes42 cited
2021A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression38 cited
2020A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses27 cited
2025A hybrid polynomial chaos expansion – Gaussian process regression method for Bayesian uncertainty quantification and sensitivity analysis18 cited
2024Nonparametric formulation of polynomial chaos expansion based on least-square support-vector machines14 cited
2021Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

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13 cited