Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling

DOI10.1016/j.cma.2019.112626zbMath1441.76147OpenAlexW2974596687MaRDI QIDQ1989088

Yanyan Li

Publication date: 24 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2019.112626

zbMATH Keywords

domain decompositionuncertainty quantificationreduced order modelspolynomial chaos expansioncomputational hemodynamicsincompressible flow in networks

Mathematics Subject Classification ID

Finite element methods applied to problems in fluid mechanics (76M10) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70) Physiological flows (76Z05) Multigrid methods; domain decomposition for initial value and initial-boundary value problems involving PDEs (65M55)

Related Items (7)

Efficient estimation of cardiac conductivities: a proper generalized decomposition approach ⋮ A mid-fidelity numerical method for blood flow in deformable vessels ⋮ Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics ⋮ A dynamic mode decomposition based reduced-order model for parameterized time-dependent partial differential equations ⋮ Model order reduction of flow based on a modular geometrical approximation of blood vessels ⋮ Geometric uncertainty in patient-specific cardiovascular modeling with convolutional dropout networks ⋮ Editorial: Special issue on uncertainty quantification, machine learning, and data-driven modeling of biological systems

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