001038624 001__ 1038624
001038624 005__ 20250310131246.0
001038624 0247_ $$2doi$$a10.1103/PhysRevResearch.7.013112
001038624 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-01595
001038624 0247_ $$2WOS$$aWOS:001415898000002
001038624 037__ $$aFZJ-2025-01595
001038624 041__ $$aEnglish
001038624 082__ $$a530
001038624 1001_ $$0P:(DE-HGF)0$$aHölscher, Leonhard$$b0$$eCorresponding author
001038624 245__ $$aQuantum-inspired fluid simulation of two-dimensional turbulence with GPU acceleration
001038624 260__ $$aCollege Park, MD$$bAPS$$c2025
001038624 3367_ $$2DRIVER$$aarticle
001038624 3367_ $$2DataCite$$aOutput Types/Journal article
001038624 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1738342271_1091
001038624 3367_ $$2BibTeX$$aARTICLE
001038624 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001038624 3367_ $$00$$2EndNote$$aJournal Article
001038624 520__ $$aTensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a spectrum of fluid phenomena, from the aerodynamics of vehicles to weather patterns. Within this quantum-inspired paradigm, velocity is encoded as matrix product states (MPS), effectively harnessing the analogy between interscale correlations of fluid dynamics and entanglement in quantum many-body physics. This particular tensor structure is also called quantics tensor train (QTT). By utilizing NVIDIA's cuQuantum library to perform parallel tensor computations on GPUs, our adaptation speeds up simulations by up to 12.1 times. This allows us to study the algorithm in terms of its applicability, scalability, and performance. By simulating two qualitatively different but commonly encountered 2D flow problems at high Reynolds numbers up to 1×107 using a fourth-order time stepping scheme, we find that the algorithm has a potential advantage over direct numerical simulations in the turbulent regime as the requirements for grid resolution increase drastically. In addition, we derive the scaling 
001038624 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001038624 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001038624 7001_ $$0P:(DE-HGF)0$$aRao, Pooja$$b1
001038624 7001_ $$0P:(DE-HGF)0$$aMüller, Lukas$$b2
001038624 7001_ $$0P:(DE-HGF)0$$aKlepsch, Johannes$$b3
001038624 7001_ $$0P:(DE-HGF)0$$aLuckow, Andre$$b4
001038624 7001_ $$0P:(DE-Juel1)194697$$aStollenwerk, Tobias$$b5
001038624 7001_ $$0P:(DE-Juel1)184630$$aWilhelm, Frank K.$$b6
001038624 773__ $$0PERI:(DE-600)3004165-X$$a10.1103/PhysRevResearch.7.013112$$gVol. 7, no. 1, p. 013112$$n1$$p013112$$tPhysical review research$$v7$$x2643-1564$$y2025
001038624 8564_ $$uhttps://juser.fz-juelich.de/record/1038624/files/PhysRevResearch.7.013112.pdf$$yOpenAccess
001038624 909CO $$ooai:juser.fz-juelich.de:1038624$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001038624 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a BMW Group$$b0
001038624 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194697$$aForschungszentrum Jülich$$b5$$kFZJ
001038624 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184630$$aForschungszentrum Jülich$$b6$$kFZJ
001038624 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5221$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001038624 9141_ $$y2025
001038624 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-02
001038624 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001038624 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2025-01-02
001038624 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-02-07T08:08:02Z
001038624 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-02-07T08:08:02Z
001038624 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2025-01-02
001038624 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-02
001038624 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001038624 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-02-07T08:08:02Z
001038624 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2025-01-02
001038624 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-02
001038624 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-02
001038624 920__ $$lyes
001038624 9201_ $$0I:(DE-Juel1)PGI-12-20200716$$kPGI-12$$lQuantum Computing Analytics$$x0
001038624 980__ $$ajournal
001038624 980__ $$aVDB
001038624 980__ $$aUNRESTRICTED
001038624 980__ $$aI:(DE-Juel1)PGI-12-20200716
001038624 9801_ $$aFullTexts