scholarly journals Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression

2021 ◽  
Vol 373 ◽  
pp. 113495
Author(s):  
Zhan Ma ◽  
Wenxiao Pan
Keyword(s):  
Dynamical Systems ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Reduced Order Modeling ◽  
Moving Boundaries ◽  
Data Driven ◽  
Reduced Order

Data-driven reduced order modeling based on tensor decompositions and its application to air-wall heat transfer in buildings

SeMA Journal ◽  
2021 ◽  
Author(s):  
M. Azaïez ◽  
T. Chacón Rebollo ◽  
M. Gómez Mármol ◽  
E. Perracchione ◽  
A. Rincón Casado ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Wall Heat Transfer ◽  
Reduced Order ◽  
Tensor Decompositions

scholarly journals Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

2020 ◽  
Vol 53 (2) ◽  
pp. 1194-1199
Author(s):  
Wenxin Xiao ◽  
Armin Lederer ◽  
Sandra Hirche
Keyword(s):  
Dynamical Systems ◽  
Gaussian Process ◽  
Gaussian Process Regression

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>

scholarly journals Data-Driven Filtered Reduced Order Modeling of Fluid Flows

2018 ◽  
Vol 40 (3) ◽  
pp. B834-B857 ◽  
Author(s):  
X. Xie ◽  
M. Mohebujjaman ◽  
L. G. Rebholz ◽  
T. Iliescu
Keyword(s):  
Fluid Flows ◽  
Reduced Order Modeling ◽  
Data Driven ◽  
Reduced Order

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
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