Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866914645390917632 |
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| author | Ortali, Giulio Demo, Nicola Rozza, Gianluigi |
| author_facet | Ortali, Giulio Demo, Nicola Rozza, Gianluigi |
| contents | 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 problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_01989 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems Ortali, Giulio Demo, Nicola Rozza, Gianluigi Numerical Analysis 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 problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem. |
| title | Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2012.01989 |