July 7, 2025 Dataset Open Access

Nis-luca van Hülst; Pia Siegl; Paul Over; Sergio Bengoechea; Tomohiro Hashizume; Mario Guillaume Cecile; Thomas Rung; Dieter Jaksch

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  <dc:creator>Nis-luca van Hülst</dc:creator>
  <dc:creator>Pia Siegl</dc:creator>
  <dc:creator>Paul Over</dc:creator>
  <dc:creator>Sergio Bengoechea</dc:creator>
  <dc:creator>Tomohiro Hashizume</dc:creator>
  <dc:creator>Mario Guillaume Cecile</dc:creator>
  <dc:creator>Thomas Rung</dc:creator>
  <dc:creator>Dieter Jaksch</dc:creator>
  <dc:date>2025-07-07</dc:date>
  <dc:description>Publication data accompanying the paper “Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates”, published in Computer Physics Communications (DOI: 10.1016/j.cpc.2026.110169). The publication and this dataset are associated with the European Union’s Horizon Europe research and innovation programme (HORIZON-CL4-2021-DIGITAL-EMERGING-02-10) under grant agreement No. 101080085, QCFD (https://doi.org/10.3030/101080085). The dataset contains the simulation data used in the publication, including reference finite-difference results and matrix-product-state (MPS) results for curvilinear incompressible flow benchmarks, covering non-rotating and rotating cylinder cases in steady and transient regimes, as well as additional validation data.</dc:description>
  <dc:identifier>https://www.fdr.uni-hamburg.de/record/17687</dc:identifier>
  <dc:identifier>10.25592/uhhfdm.17687</dc:identifier>
  <dc:identifier>oai:fdr.uni-hamburg.de:17687</dc:identifier>
  <dc:relation>doi:10.25592/uhhfdm.17686</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>QCFD</dc:subject>
  <dc:subject>Tensor Networks</dc:subject>
  <dc:subject>Tensor Trains</dc:subject>
  <dc:subject>CFD</dc:subject>
  <dc:subject>Curvilinear Coordinates</dc:subject>
  <dc:subject>Body-Fitted Grids</dc:subject>
  <dc:subject>Fluid Dynamics</dc:subject>
  <dc:subject>MPS</dc:subject>
  <dc:subject>DMRG</dc:subject>
  <dc:title>Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
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