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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{"DOI":"10.25592/uhhfdm.17687","abstract":"<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>","author":[{"family":"Nis-luca van H\u00fclst"},{"family":"Pia Siegl"},{"family":"Paul Over"},{"family":"Sergio Bengoechea"},{"family":"Tomohiro Hashizume"},{"family":"Mario Guillaume Cecile"},{"family":"Thomas Rung"},{"family":"Dieter Jaksch"}],"id":"17687","issued":{"date-parts":[[2025,7,7]]},"title":"Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates","type":"dataset"}