The invention discloses an
uncertainty quantification method based on a derivative enhanced
Gaussian process, and belongs to the technical field of
uncertainty quantification, and the method comprises the steps: S1, obtaining a training sample set; s2, first-order derivatives and second-order derivatives of the aerodynamic performance parameters for the geometric parameters and the working conditions are obtained; s3, constructing a derivative enhanced
Gaussian process model by using the training sample set obtained in the step S1; s4, verifying the prediction performance of the derivative enhanced
Gaussian process model through
cross validation, and evaluating the accuracy of
uncertainty quantification; and S5, performing stochastic
simulation in the neighborhood space of the
initial sample point, predicting an objective function of a stochastic
simulation sample by using the derivative enhanced
Gaussian process model, and calculating a robustness index to complete uncertainty quantization. According to the uncertainty quantification method based on the derivative enhanced
Gaussian process, the derivative information is fused into the
Gaussian process, the global precision of the model and the local precision near the sample point are considered, and the uncertainty quantification precision can be improved.