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pymor/pymor: pyMOR 2020.2.0

Stephan Rave; Petar Mlinarić; Tim Keil; Felix Schindler; Hendrik Kleikamp; Michael Laier; Andreas Buhr; Michael Schaefer; G. D. McBain; Julia Brunken; Meret Behrens; Luca Mechelli; 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>Tim Keil</dc:creator>
  <dc:creator>Felix Schindler</dc:creator>
  <dc:creator>Hendrik Kleikamp</dc:creator>
  <dc:creator>Michael Laier</dc:creator>
  <dc:creator>Andreas Buhr</dc:creator>
  <dc:creator>Michael Schaefer</dc:creator>
  <dc:creator>G. D. McBain</dc:creator>
  <dc:creator>Julia Brunken</dc:creator>
  <dc:creator>Meret Behrens</dc:creator>
  <dc:creator>Luca Mechelli</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:

Parameter derivatives of solutions and outputs
Neural network reductor for non-stationary problems
New tutorials

You can read the full release notes at</dc:description>
  <dc:title>pymor/pymor: pyMOR 2020.2.0</dc:title>
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