Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
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.
Authors
- Pia Siegl (ORCID: https://orcid.org/0000-0003-2249-8121)
- Sergio Bengoechea (ORCID: https://orcid.org/0009-0001-8205-5878)
- Dieter Jaksch (ORCID: https://orcid.org/0000-0002-9704-3941)
- Paul Over (ORCID: https://orcid.org/0000-0001-7436-5254)
- Thomas Rung (ORCID: https://orcid.org/0000-0002-3454-1804)
- Tomohiro Hashizume (ORCID: https://orcid.org/0000-0002-7154-5417)
- Nis-Luca van Hülst
- Mario Guillaume Cecile
Publication Details
- Journal
- Computer Physics Communications
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1016/j.cpc.2026.110169
- Primary Topic
- Quantum many-body systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Deutsches Zentrum für Luft- und Raumfahrt
- HORIZON EUROPE Framework Programme