February 11, 2026 Dataset Open Access

Rustamov, Jeyhun

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  <identifier identifierType="DOI">10.14278/rodare.4504</identifier>
  <creators>
    <creator>
      <creatorName>Rustamov, Jeyhun</creatorName>
      <givenName>Jeyhun</givenName>
      <familyName>Rustamov</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4803-2461</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title>VIPR Reflectometry Model: Neural Spline Flow for Neutron Reflectometry Inference (MARIA Dataset)</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2026</publicationYear>
  <dates>
    <date dateType="Issued">2026-02-11</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/4504</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-43325</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPartOf">10.57967/hf/8498</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.4503</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This dataset provides a pre-trained Neural Spline Flow (NSF) model for probabilistic inference in neutron reflectometry, developed within the VIPR framework.&lt;/p&gt;

&lt;p&gt;The model estimates posterior distributions of thin-film parameters (e.g., thickness, SLD, roughness) directly from reflectivity curves, enabling fast uncertainty quantification and detection of multimodal solutions. It is configured for data from the MARIA reflectometer and corresponds to the probabilistic workflow presented in the associated VIPR publication .&lt;/p&gt;

&lt;p&gt;Contents: model weights and configuration file.&lt;br&gt;
Usage: designed for execution within VIPR via YAML-defined inference workflows.&lt;/p&gt;</description>
  </descriptions>
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