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pymor/pymor: pyMOR 2020.1.1

Stephan Rave; Petar Mlinarić; Felix Schindler; Hendrik Kleikamp; Tim Keil; Michael Laier; Andreas Buhr; Michael Schaefer; Julia Brunken; René Fritze; 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>Felix Schindler</dc:creator>
  <dc:creator>Hendrik Kleikamp</dc:creator>
  <dc:creator>Tim Keil</dc:creator>
  <dc:creator>Michael Laier</dc:creator>
  <dc:creator>Andreas Buhr</dc:creator>
  <dc:creator>Michael Schaefer</dc:creator>
  <dc:creator>Julia Brunken</dc:creator>
  <dc:creator>René Fritze</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:

	Non-intrusive model order reduction using artificial neural networks.
	The subspace accelerated dominant pole algorithm (SAMDP).
	The implicitly restarted Arnoldi method for eigenvalue computation.
	Parameter handling in pyMOR has been simplified.
	A new series of hands-on tutorials.

You can read the full release notes at</dc:description>
  <dc:title>pymor/pymor: pyMOR 2020.1.1</dc:title>
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