A comparison of a traditional geostatistical regression approach and a general Gaussian process approach for spatial prediction

Stat ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 228-239 ◽  
Author(s):  
Stanley Leung ◽  
Daniel Cooley
Keyword(s):  
Gaussian Process ◽  
Spatial Prediction ◽  
Process Approach ◽  
Regression Approach

scholarly journals A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Giulio Ortali ◽  
◽  
Nicola Demo ◽  
Gianluigi Rozza ◽  
Keyword(s):  
Gaussian Process ◽  
Stokes Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Data Driven ◽  
Regression Approach ◽  
Data Driven Approach ◽  
Naval Engineering ◽  
Proper Orthogonal ◽  
Industrial Problem

<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>

Gaussian process approach for metric learning

2019 ◽  
Vol 87 ◽  
pp. 17-28 ◽  
Author(s):  
Ping Li ◽  
Songcan Chen
Keyword(s):  
Gaussian Process ◽  
Metric Learning ◽  
Process Approach
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