Software Open Access

VIPR (Versatile Inverse Problem Software Framework) unified demonstrator

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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      <affiliation>Helm &amp; Walter IT-Solutions GmbH, Dresden, Germany</affiliation>
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      <creatorName>Bornschein, Jens</creatorName>
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      <givenName>Marina</givenName>
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      <affiliation>Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich GmbH, Jülich, Germany</affiliation>
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      <creatorName>Gerlach, Alexander</creatorName>
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      <affiliation>Universität Tübingen, Tübingen, Germany</affiliation>
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      <creatorName>Häusler, Stefan</creatorName>
      <givenName>Stefan</givenName>
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      <creatorName>Helm, Bernd</creatorName>
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      <affiliation>Universität Tübingen, Tübingen, Germany</affiliation>
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      <creatorName>Juzak, Robert</creatorName>
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      <creatorName>Pandit, Vedhas</creatorName>
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      <creatorName>Schreiber, Frank</creatorName>
      <givenName>Frank</givenName>
      <familyName>Schreiber</familyName>
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      <affiliation>Universität Tübingen, Tübingen, Germany</affiliation>
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      <creatorName>Mothes, Nico</creatorName>
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      <creatorName>Kelling, Jeffrey</creatorName>
      <givenName>Jeffrey</givenName>
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      <affiliation>Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany</affiliation>
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  <titles>
    <title>VIPR (Versatile Inverse Problem Software Framework) unified demonstrator</title>
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  <publisher>Rodare</publisher>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject>Ill-posed Inverse Problems</subject>
    <subject>Reflectometry</subject>
    <subject>Machine Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2026-04-29</date>
  </dates>
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  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/4633</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementedBy">10.14278/rodare.4504</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementedBy">10.57967/hf/8498</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-43563</relatedIdentifier>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <version>rodare-1</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/LGPL-3.0">GNU Lesser General Public License v3.0 or later</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
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Publication date:
April 29, 2026
DOI:
10.14278/rodare.4633

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Keyword(s):
Ill-posed Inverse Problems Reflectometry Machine Learning
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https://www.hzdr.de/publications/Publ-43563
Supplementary material:
10.14278/rodare.4504, 10.57967/hf/8498
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GNU Lesser General Public License v3.0 or later

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