References

• 2013Uncertainty Quantification: Theory, Implementation, and Applications1,147 cited
• 2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion451 cited
• 2009Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design371 cited
• 2009Efficient Uncertainty Quantification Using Gradient-Enhanced Kriging96 cited
• 2013Fundamental limitations of polynomial chaos for uncertainty quantification in systems with intermittent instabilities70 cited
• 2019A Fast Polynomial Chaos Expansion for Uncertainty Quantification in Stochastic Electromagnetic Problems49 cited
• 2022Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes42 cited
• 2019Comparison of intrusive and nonintrusive polynomial chaos expansion-based approaches for high dimensional parametric uncertainty quantification and propagation40 cited
• 2021A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression38 cited
• 2005Fast and reliable methods for determining the evolution of uncertain parameters in differential equations29 cited
• 2021Active Learning Kriging Model With Adaptive Uniform Design for Time-Dependent Reliability Analysis25 cited
• 2007Fast methods for determining the evolution of uncertain parameters in reaction-diffusion equations21 cited
• 2020Uncertainty Quantification of Waveguide Dispersion Using Sparse Grid Stochastic Testing