A screening-based gradient-enhanced Gaussian process regression model for multi-fidelity data fusion. (October 2021) - Lin, Quan; Hu, Dawei; Hu, Jiexiang; Cheng, Yuansheng; Zhou, Qi
Abstract: The prediction accuracy of multi-fidelity models can be enhanced by incorporating gradient formation. However, the computational complexity would increase dramatically as the number of design variables increase. In this work, a gradient-enhanced multi-fidelity Gaussian process model using a portion of gradients (PGEMFGP) is proposed. To be specific, a Bayesian Gaussian process regression model for multi-fidelity (MF) data fusion is developed, which incorporates high-fidelity (HF) and low-fidelity (LF) responses, as well as the corresponding gradients. A screening technique based on distance correlation is applied to select a portion of gradients of the low-fidelity model so that the modeling complexity can be greatly reduced. The merit of the proposed method is tested with six numerical examples ranging from 10-D to 30-D, as well as an aerodynamic airfoil case with 18 design variables. The proposed method is compared to two other existing gradient-enhanced Gaussian process-based models. It is shown that the modeling efficiency of the proposed model is dramatically improved compared to the original gradient-enhanced multi-fidelity Gaussian process model, while the loss of the prediction accuracy can be almost negligible. In consequence, it can be a promising approach for gradient-enhanced models dealing with multi-fidelity data.