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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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3579256", 
  "title": "pymor/pymor: pyMOR 2019.2.0", 
  "issued": {
    "date-parts": [
      [
        2019, 
        12, 
        16
      ]
    ]
  }, 
  "abstract": "<p>pyMOR is a software library for building model order reduction \napplications with the Python programming language. Implemented \nalgorithms include reduced basis methods for parametric linear and \nnon-linear problems, as well as system-theoretic methods such as \nbalanced truncation or IRKA. All algorithms in pyMOR are formulated in \nterms of abstract interfaces for seamless integration with external PDE \nsolver packages. Moreover, pure Python implementations of finite element \nand finite volume discretizations using the NumPy/SciPy scientific \ncomputing stack are provided for getting started quickly.</p>\n<p>Highlights of this release are:</p>\n<ul>\n<li>Improved model and reductor design makes pyMOR easier to extend.</li>\n<li>Extended VectorArray interface with generic complex number support.</li>\n<li>Improved and extended system-theoretic MOR methods.</li>\n<li>Builtin support for model outputs and parameter sensitivities.</li>\n</ul>", 
  "author": [
    {
      "family": "Stephan Rave"
    }, 
    {
      "family": "Petar Mlinari\u0107"
    }, 
    {
      "family": "Felix Schindler"
    }, 
    {
      "family": "Michael Laier"
    }, 
    {
      "family": "Andreas Buhr"
    }, 
    {
      "family": "Tim Keil"
    }, 
    {
      "family": "Michael Schaefer"
    }, 
    {
      "family": "Julia Brunken"
    }, 
    {
      "family": "cabuze"
    }
  ], 
  "version": "2019.2.0", 
  "type": "article", 
  "id": "3579256"
}
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