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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{"@context":"https://schema.org/","@id":"http://doi.org/10.25592/uhhfdm.17687","@type":"Dataset","creator":[{"@id":"https://orcid.org/0009-0004-9893-3614","@type":"Person","affiliation":"Universit\u00e4t Hamburg","name":"Nis-luca van H\u00fclst"},{"@id":"https://orcid.org/0000-0003-2249-8121","@type":"Person","affiliation":"German Aerospace Center","name":"Pia Siegl"},{"@id":"https://orcid.org/0000-0001-7436-5254","@type":"Person","affiliation":"Hamburg University of Technology","name":"Paul Over"},{"@id":"https://orcid.org/0009-0001-8205-5878","@type":"Person","affiliation":"Hamburg University of Technology","name":"Sergio Bengoechea"},{"@id":"https://orcid.org/0000-0002-7154-5417","@type":"Person","affiliation":"University of Queensland, University of Strathclyde, Universit\u00e4t Hamburg","name":"Tomohiro Hashizume"},{"@id":"https://orcid.org/0000-0002-2076-6236","@type":"Person","affiliation":"CY Cergy Paris University, University of Mauritius","name":"Mario Guillaume Cecile"},{"@id":"https://orcid.org/0000-0002-3454-1804","@type":"Person","affiliation":"Hamburg University of Technology","name":"Thomas Rung"},{"@id":"https://orcid.org/0000-0002-9704-3941","@type":"Person","affiliation":"University of Hamburg, University of Oxford","name":"Dieter Jaksch"}],"datePublished":"2025-07-07","description":"<p>Publication data accompanying the paper &ldquo;Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates&rdquo;, published in Computer Physics Communications (DOI: 10.1016/j.cpc.2026.110169).&nbsp;The publication and this dataset are associated with the European Union&rsquo;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.</p>","distribution":[{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/48d0258c-b19e-4eee-a835-3fcec58d392f/Publication_Data.zip","encodingFormat":"zip"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/48d0258c-b19e-4eee-a835-3fcec58d392f/README.md","encodingFormat":"md"}],"identifier":"http://doi.org/10.25592/uhhfdm.17687","keywords":["QCFD","Tensor Networks","Tensor Trains","CFD","Curvilinear Coordinates","Body-Fitted Grids","Fluid Dynamics","MPS","DMRG"],"license":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates","url":"https://www.fdr.uni-hamburg.de/record/17687"}