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pyMOR - Reduced Order Modeling with Python
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This paper shows recent developments in pyMOR, in particular the addition of system‐theoretic methods. All methods are implemented using pyMOR's abstract interfaces, which allows the application to partial differential equation (PDE) models implemented with third‐party libraries. We demonstrate this by applying balanced truncation to a PDE model discretized in FEniCS.
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 third-party 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.
Parametric Model Order Reduction Using pyMOR
- P Mlinarić
- S Rave
- J Saak
Mlinarić, P., Rave, S., and Saak, J. (2021). Parametric
Model Order Reduction Using pyMOR. In P. Benner,
T. Breiten, H. Faßbender, M. Hinze, T. Stykel, and
R. Zimmermann (eds.), Model Reduction of Complex
Dynamical Systems, International Series of Numerical
Mathematics, 357-367. Springer International Publishing, Cham. doi:10.1007/978-3-030-72983-7 17.