scholarly journals Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems

2017 ◽  
Vol 345 ◽  
pp. 40-55 ◽  
Author(s):  
Zhong Yi Wan ◽  
Themistoklis P. Sapsis
2020 ◽  
Vol 53 (2) ◽  
pp. 1194-1199
Author(s):  
Wenxin Xiao ◽  
Armin Lederer ◽  
Sandra Hirche

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

2020 ◽  
Vol 136 ◽  
pp. 109924 ◽  
Author(s):  
Ricardo Manuel Arias Velásquez ◽  
Jennifer Vanessa Mejía Lara

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

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