Dataset Open Access

Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates

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

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.

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Publication date:
July 7, 2025
DOI:
10.25592/uhhfdm.17687

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Keyword(s):
QCFD Tensor Networks Tensor Trains CFD Curvilinear Coordinates Body-Fitted Grids Fluid Dynamics MPS DMRG
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License (for files):
Creative Commons Attribution 4.0 International

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