A screening-based gradient-enhanced Gaussian process regression model for multi-fidelity data fusion |
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| Affiliation: | 1. School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;2. Wuhan Second Ship Design and Research Institute, Wuhan, 430064 Hubei, China;3. School of Naval Architecture and Ocean Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;1. Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA;2. Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA;1. School of Management, Harbin Institute of Technology, Harbin 150001, China;2. School of Architecture, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong 518055, China;3. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China;1. Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 770072 Xi''an, China;2. Department of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi’an 710038, China;3. Department of Cardiology, Tangdu Hospital, Fourth Military Medical University, Xi''an 710038, China;4. School of Design and Art, Shaanxi University of Science & Technology, Xi''an 710021, China;5. School of Art, Taiyuan University of Science and Technology, Tai''yuan 030024, China;6. School of Mechanical Engineering, Taiyuan University of Science and Technology, Tai''yuan 030024, China;1. School of Economics and Management, Tongji University, Shanghai 200092, PR China;2. Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, PR China |
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| 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. |
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