%0 Generic %1 milk2015pymor %A Milk, René %A Rave, Stephan %A Schindler, Felix %D 2015 %K 35-04-pdes-explicit-machine-computation-programs 35j20-variational-methods-for-second-order-elliptic-equations 35l03-ivps-first-order-hyperbolic-equations 65n30-pdes-bvps-finite-elements 65y05-parallel-computation 68n01-software-general 65-04-numerical-analysis-software-source-code %R 10.1137/15M1026614 %T pyMOR - Generic Algorithms and Interfaces for Model Order Reduction %U http://arxiv.org/abs/1506.07094 %X 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.

@misc{milk2015pymor, 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.}, added-at = {2019-03-29T00:42:44.000+0100}, author = {Milk, René and Rave, Stephan and Schindler, Felix}, biburl = {https://www.bibsonomy.org/bibtex/2ea38f0f893ed99c80579265cb9b72dd4/gdmcbain}, doi = {10.1137/15M1026614}, interhash = {3b170efc95ed4106cf152f87d2c943c6}, intrahash = {ea38f0f893ed99c80579265cb9b72dd4}, keywords = {35-04-pdes-explicit-machine-computation-programs 35j20-variational-methods-for-second-order-elliptic-equations 35l03-ivps-first-order-hyperbolic-equations 65n30-pdes-bvps-finite-elements 65y05-parallel-computation 68n01-software-general 65-04-numerical-analysis-software-source-code}, note = {cite arxiv:1506.07094}, timestamp = {2020-08-24T06:59:39.000+0200}, title = {pyMOR - Generic Algorithms and Interfaces for Model Order Reduction}, url = {http://arxiv.org/abs/1506.07094}, year = 2015 }