April 29, 2026 Software Open Access

Creutzburg, Sascha; Rustamov, Jeyhun; Bornschein, Jens; Ganeva, Marina; Gerlach, Alexander; Häusler, Stefan; Helm, Bernd; Hinderhofer, Alexander; Juzak, Robert; Koutsioumpas, Alexandros; Munteanu, Valentin; Pandit, Vedhas; Schreiber, Frank; Mothes, Nico; Kelling, Jeffrey

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  <dc:creator>Creutzburg, Sascha</dc:creator>
  <dc:creator>Rustamov, Jeyhun</dc:creator>
  <dc:creator>Bornschein, Jens</dc:creator>
  <dc:creator>Ganeva, Marina</dc:creator>
  <dc:creator>Gerlach, Alexander</dc:creator>
  <dc:creator>Häusler, Stefan</dc:creator>
  <dc:creator>Helm, Bernd</dc:creator>
  <dc:creator>Hinderhofer, Alexander</dc:creator>
  <dc:creator>Juzak, Robert</dc:creator>
  <dc:creator>Koutsioumpas, Alexandros</dc:creator>
  <dc:creator>Munteanu, Valentin</dc:creator>
  <dc:creator>Pandit, Vedhas</dc:creator>
  <dc:creator>Schreiber, Frank</dc:creator>
  <dc:creator>Mothes, Nico</dc:creator>
  <dc:creator>Kelling, Jeffrey</dc:creator>
  <dc:date>2026-04-29</dc:date>
  <dc:description>VIPR (Versatile Inverse Problem Software Framework) is a plugin-based framework for reproducible machine-learning-driven solutions to scientific inverse problems. It addresses ill-posed reconstruction tasks caused by loss of phase information during measurement, where direct inversion is not possible. It implements a modular architecture with domain-specific plugins to produce configurable machine learning workflows, including both deterministic and probabilistic models. Workflows are defined via declarative YAML configurations and can be executed through a command-line interface or a containerized web application. For a given experimental dataset, VIPR produces standardized analysis artifacts, including visualizations and statistical summaries.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4633</dc:identifier>
  <dc:identifier>10.14278/rodare.4633</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4633</dc:identifier>
  <dc:relation>doi:10.14278/rodare.4504</dc:relation>
  <dc:relation>doi:10.57967/hf/8498</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-43563</dc:relation>
  <dc:relation>doi:10.14278/rodare.4632</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/matter</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://opensource.org/licenses/LGPL-3.0</dc:rights>
  <dc:subject>Ill-posed Inverse Problems</dc:subject>
  <dc:subject>Reflectometry</dc:subject>
  <dc:subject>Machine Learning</dc:subject>
  <dc:title>VIPR (Versatile Inverse Problem Software Framework) unified demonstrator</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>