A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics
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<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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2021 ◽
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2021 ◽
Vol 147
(4)
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pp. 04021008
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2013 ◽
Vol 7
(2)
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pp. 133-136
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