scholarly journals Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

2020 ◽  
Vol 370 ◽  
pp. 113269
Author(s):  
D.G. Giovanis ◽  
M.D. Shields
Keyword(s):  
Gaussian Process ◽  
Grassmann Manifold ◽  
Gaussian Process Regression ◽  
Data Driven ◽  
High Dimensional ◽  
Dimensional Models

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>

Uniform Design–Based Gaussian Process Regression for Data-Driven Rapid Fragility Assessment of Bridges

2021 ◽  
Vol 147 (4) ◽  
pp. 04021008
Author(s):  
Yutao Pang ◽  
Xiaoyong Zhou ◽  
Wei He ◽  
Jian Zhong ◽  
Ouyang Hui
Keyword(s):  
Gaussian Process ◽  
Uniform Design ◽  
Gaussian Process Regression ◽  
Data Driven

A data driven BRDF model based on Gaussian process regression

2013 ◽  
Author(s):  
Zhuang Tian ◽  
Dongdong Weng ◽  
Jianying Hao ◽  
Yupeng Zhang ◽  
Dandan Meng
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Data Driven ◽  
Model Based
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