pyMOR - Generic Algorithms and Interfaces for Model Order Reduction
Reduced basis methods are projection-based model order reduction techniques for reducing the computational complexity of solving parametrized partial differential equation problems. In this work we discuss the design of pyMOR, a freely available software library of model order reduction algorithms,...
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| Published in | arXiv.org |
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| Main Authors | , , |
| Format | Paper Journal Article |
| Language | English |
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Ithaca
Cornell University Library, arXiv.org
31.03.2016
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| ISSN | 2331-8422 |
| DOI | 10.48550/arxiv.1506.07094 |
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| Abstract | Reduced basis methods are projection-based model order reduction techniques for reducing the computational complexity of solving parametrized partial differential equation problems. In this work we discuss the design of pyMOR, a freely available software library of model order reduction algorithms, in particular reduced basis methods, implemented with the Python programming language. As its main design feature, all reduction algorithms in pyMOR are implemented generically via operations on well-defined vector array, operator and discretization interface classes. This allows for an easy integration with existing open-source high-performance partial differential equation solvers without adding any model reduction specific code to these solvers. Besides an in-depth discussion of pyMOR's design philosophy and architecture, we present several benchmark results and numerical examples showing the feasibility of our approach. |
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| AbstractList | Reduced basis methods are projection-based model order reduction techniques for reducing the computational complexity of solving parametrized partial differential equation problems. In this work we discuss the design of pyMOR, a freely available software library of model order reduction algorithms, in particular reduced basis methods, implemented with the Python programming language. As its main design feature, all reduction algorithms in pyMOR are implemented generically via operations on well-defined vector array, operator and discretization interface classes. This allows for an easy integration with existing open-source high-performance partial differential equation solvers without adding any model reduction specific code to these solvers. Besides an in-depth discussion of pyMOR's design philosophy and architecture, we present several benchmark results and numerical examples showing the feasibility of our approach. SIAM J. Sci. Comput., 38 (2016), pp. S194-S216 Reduced basis methods are projection-based model order reduction techniques for reducing the computational complexity of solving parametrized partial differential equation problems. In this work we discuss the design of pyMOR, a freely available software library of model order reduction algorithms, in particular reduced basis methods, implemented with the Python programming language. As its main design feature, all reduction algorithms in pyMOR are implemented generically via operations on well-defined vector array, operator and discretization interface classes. This allows for an easy integration with existing open-source high-performance partial differential equation solvers without adding any model reduction specific code to these solvers. Besides an in-depth discussion of pyMOR's design philosophy and architecture, we present several benchmark results and numerical examples showing the feasibility of our approach. |
| Author | Rave, Stephan Milk, René Schindler, Felix |
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| BackLink | https://doi.org/10.1137/15M1026614$$DView published paper (Access to full text may be restricted) https://doi.org/10.48550/arXiv.1506.07094$$DView paper in arXiv |
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| Copyright | 2016. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://arxiv.org/licenses/nonexclusive-distrib/1.0 |
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| Snippet | Reduced basis methods are projection-based model order reduction techniques for reducing the computational complexity of solving parametrized partial... SIAM J. Sci. Comput., 38 (2016), pp. S194-S216 Reduced basis methods are projection-based model order reduction techniques for reducing the computational... |
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| Title | pyMOR - Generic Algorithms and Interfaces for Model Order Reduction |
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