Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters

DOI10.1007/s10915-022-02082-5arXiv2203.13682MaRDI QIDQ6101879

Michele Girfoglio, Gianluigi Rozza, Pierfrancesco Siena, Francesco Ballarin

Publication date: 5 May 2023

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.13682

zbMATH Keywords

finite volume methodneural networkincompressible Navier-Stokes equationsproper orthogonal decompositionmachine learningmodal coefficient

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Navier-Stokes equations for incompressible viscous fluids (76D05) Finite volume methods applied to problems in fluid mechanics (76M12) Physiological flows (76Z05) Physiological flow (92C35)

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