Gaussian process regression approach for robust design and yield enhancement of self-assembled nanostructures

2018 ◽  
Vol 88-90 ◽  
pp. 85-90
Author(s):  
Pham Luu Trung Duong ◽  
Xuechu Xu ◽  
Qing Yang ◽  
Nagarajan Raghavan
Keyword(s):  
Gaussian Process ◽  
Robust Design ◽  
Gaussian Process Regression ◽  
Yield Enhancement ◽  
Regression Approach ◽  
Self Assembled

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>

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