pyROM: A computational framework for reduced order modeling
Model reduction techniques reduce the overall complexity of dynamic systems and allow to speed up simulations of their behavior several orders of magnitude while retaining good accuracy. Despite being useful to obtain real-time simulations and apply control strategies, only few freely available soft...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/74806 |
| _version_ | 1848763377717870592 |
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| author | Puzyrev, Vladimir Ghommem, M. Meka, S. |
| author_facet | Puzyrev, Vladimir Ghommem, M. Meka, S. |
| author_sort | Puzyrev, Vladimir |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Model reduction techniques reduce the overall complexity of dynamic systems and allow to speed up simulations of their behavior several orders of magnitude while retaining good accuracy. Despite being useful to obtain real-time simulations and apply control strategies, only few freely available software implementations of model reduction techniques have been reported in the literature. Furthermore, the use of these tools tends to be only for a limited range of dynamic problems, mostly related to fluid flows, and to deal with relatively small systems and datasets. In this paper, we build a portable, user-friendly, and open source computational framework, namely pyROM, implementing model reduction techniques in the Python programming language. This tool is designed to satisfy the needs of wide range of users to deploy model reduction for reproducing the dynamic response of high-dimensional models with good accuracy while achieving significant computational savings. The framework is designed in an object-oriented way to be easy to use and extend and employs visualization tools from various Python libraries such as Matplotlib, Mayavi, and Bokeh. Several numerical examples using modern spatial discretization methods such as the finite element method, the isogeometric analysis, the meshless point collocation method, and the generalized multiscale finite element method demonstrate the performance of the developed computational tool and the capabilities of model reduction methods to handle different engineering problems. |
| first_indexed | 2025-11-14T11:02:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-74806 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:02:30Z |
| publishDate | 2019 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-748062019-08-14T01:25:11Z pyROM: A computational framework for reduced order modeling Puzyrev, Vladimir Ghommem, M. Meka, S. Model reduction techniques reduce the overall complexity of dynamic systems and allow to speed up simulations of their behavior several orders of magnitude while retaining good accuracy. Despite being useful to obtain real-time simulations and apply control strategies, only few freely available software implementations of model reduction techniques have been reported in the literature. Furthermore, the use of these tools tends to be only for a limited range of dynamic problems, mostly related to fluid flows, and to deal with relatively small systems and datasets. In this paper, we build a portable, user-friendly, and open source computational framework, namely pyROM, implementing model reduction techniques in the Python programming language. This tool is designed to satisfy the needs of wide range of users to deploy model reduction for reproducing the dynamic response of high-dimensional models with good accuracy while achieving significant computational savings. The framework is designed in an object-oriented way to be easy to use and extend and employs visualization tools from various Python libraries such as Matplotlib, Mayavi, and Bokeh. Several numerical examples using modern spatial discretization methods such as the finite element method, the isogeometric analysis, the meshless point collocation method, and the generalized multiscale finite element method demonstrate the performance of the developed computational tool and the capabilities of model reduction methods to handle different engineering problems. 2019 Journal Article http://hdl.handle.net/20.500.11937/74806 10.1016/j.jocs.2018.12.004 Elsevier Ltd restricted |
| spellingShingle | Puzyrev, Vladimir Ghommem, M. Meka, S. pyROM: A computational framework for reduced order modeling |
| title | pyROM: A computational framework for reduced order modeling |
| title_full | pyROM: A computational framework for reduced order modeling |
| title_fullStr | pyROM: A computational framework for reduced order modeling |
| title_full_unstemmed | pyROM: A computational framework for reduced order modeling |
| title_short | pyROM: A computational framework for reduced order modeling |
| title_sort | pyrom: a computational framework for reduced order modeling |
| url | http://hdl.handle.net/20.500.11937/74806 |