most citedGaussian Process Regression for Uncertainty Quantification: An Introductory Tutorial

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Gaussian Process Regression for Uncertainty Quantification: An Introductory Tutorial

Jinglai Li, Hongqiao Wang

Uncertainty Quantification (UQ) is essential for the reliable application of computational models in engineering and science. Among surrogate modeling techniques, Gaussian Process…

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Model-Embedded Gaussian Process Regression for Parameter Estimation in Dynamical System

Ying Zhou, Jinglai Li, Xiang Zhou +1

Identifying dynamical system (DS) is a vital task in science and engineering. Traditional methods require numerous calls to the DS solver, rendering likelihood-based or least-squar…