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pymor/pymor: pyMOR 2019.2.0

Stephan Rave; Petar Mlinarić; Felix Schindler; Michael Laier; Andreas Buhr; Tim Keil; Michael Schaefer; Julia Brunken; cabuze

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Stephan Rave</dc:creator>
  <dc:creator>Petar Mlinarić</dc:creator>
  <dc:creator>Felix Schindler</dc:creator>
  <dc:creator>Michael Laier</dc:creator>
  <dc:creator>Andreas Buhr</dc:creator>
  <dc:creator>Tim Keil</dc:creator>
  <dc:creator>Michael Schaefer</dc:creator>
  <dc:creator>Julia Brunken</dc:creator>
  <dc:description>pyMOR is a software library for building model order reduction 
applications with the Python programming language. Implemented 
algorithms include reduced basis methods for parametric linear and 
non-linear problems, as well as system-theoretic methods such as 
balanced truncation or IRKA. All algorithms in pyMOR are formulated in 
terms of abstract interfaces for seamless integration with external PDE 
solver packages. Moreover, pure Python implementations of finite element 
and finite volume discretizations using the NumPy/SciPy scientific 
computing stack are provided for getting started quickly.
Highlights of this release are:

Improved model and reductor design makes pyMOR easier to extend.
Extended VectorArray interface with generic complex number support.
Improved and extended system-theoretic MOR methods.
Builtin support for model outputs and parameter sensitivities.
  <dc:title>pymor/pymor: pyMOR 2019.2.0</dc:title>
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