On Data-driven Attack-resilient Gaussian Process Regression for Dynamic Systems

Author(s):  
Hunmin Kim ◽  
Pinyao Guo ◽  
Minghui Zhu ◽  
Peng Liu
2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Giulio Ortali ◽  
◽  
Nicola Demo ◽  
Gianluigi Rozza ◽  

<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>


2021 ◽  
Vol 147 (4) ◽  
pp. 04021008
Author(s):  
Yutao Pang ◽  
Xiaoyong Zhou ◽  
Wei He ◽  
Jian Zhong ◽  
Ouyang Hui

2013 ◽  
Author(s):  
Zhuang Tian ◽  
Dongdong Weng ◽  
Jianying Hao ◽  
Yupeng Zhang ◽  
Dandan Meng

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222256-222266
Author(s):  
Pham Luu Trung Duong ◽  
Shaista Hussain ◽  
Mark Hyunpong Jhon ◽  
Nagarajan Raghavan

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