A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics
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, t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-05-01
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| Series: | Mathematics in Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mine.2022021?viewType=HTML |
| _version_ | 1811305619538313216 |
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| author | Giulio Ortali Nicola Demo Gianluigi Rozza |
| author_facet | Giulio Ortali Nicola Demo Gianluigi Rozza |
| author_sort | Giulio Ortali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:28:34Z |
| format | Article |
| id | doaj.art-071ce1b66a15486e9dae59f7b0b5477a |
| institution | Directory Open Access Journal |
| issn | 2640-3501 |
| language | English |
| last_indexed | 2024-04-13T08:28:34Z |
| publishDate | 2022-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematics in Engineering |
| spelling | doaj.art-071ce1b66a15486e9dae59f7b0b5477a2022-12-22T02:54:19ZengAIMS PressMathematics in Engineering2640-35012022-05-014311610.3934/mine.2022021A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamicsGiulio Ortali0Nicola Demo1Gianluigi Rozza21. Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, Italy 2. Department of Applied Physics, Eindhoven University of Technology, The Netherlands1. Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, Italy1. Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, ItalyThis 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.https://www.aimspress.com/article/doi/10.3934/mine.2022021?viewType=HTMLdata-driven methodreduced order modelinggaussian process regressionparametric design problem |
| spellingShingle | Giulio Ortali Nicola Demo Gianluigi Rozza A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics Mathematics in Engineering data-driven method reduced order modeling gaussian process regression parametric design problem |
| title | A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics |
| title_full | A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics |
| title_fullStr | A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics |
| title_full_unstemmed | A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics |
| title_short | A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics |
| title_sort | gaussian process regression approach within a data driven pod framework for engineering problems in fluid dynamics |
| topic | data-driven method reduced order modeling gaussian process regression parametric design problem |
| url | https://www.aimspress.com/article/doi/10.3934/mine.2022021?viewType=HTML |
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