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Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity.
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Neural network modeling for near wall turbulent flow
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Hemorheological disorders in diabetes mellitus
Y. I. Cho, M. P. Mooney, and D. J. Cho · 2008
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L. Antiga and D. A. Steinman · 2009
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Flow instability and wall shear stress variation in intracranial aneurysms
H. Baek, M. V. Jayaraman, P. D. Richardson, and G. E. Karniadakis · 2010
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Wall shear stress and near-wall convective transport: Comparisons with vascular remodelling in a peripheral graft anastomosis
A. M. Gambaruto, D. J. Doorly, and T. Yamaguchi · 2010
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Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease
H. Samady, P. Eshtehardi, M. C. McDaniel, J. Suo, S. S. Dhawan, C. Maynard, L. H. Timmins, A. A. Quyyumi, and D. P. Giddens · 2011
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K. Leiderman and A. L. Fogelson · 2011
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Association of hemodynamic characteristics and cerebral aneurysm rupture
J. R. Cebral, F. Mut, J. Weir, and C. M. Putman · 2011
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Characterization of the transport topology in patient-specific abdominal aortic aneurysm models
A. Arzani and S. C. Shadden · 2012
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A. Logg, K. A. Mardal, and G. Wells · 2012
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Potential fluid mechanic pathways of platelet activation
S. C. Shadden and S. Hendabadi · 2013
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Lagrangian wall shear stress structures and near-wall transport in high-Schmidt-number aneurysmal flows
A. Arzani, A. M. Gambaruto, G. Chen, and S. C. Shadden · 2016
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Characterizations and correlations of wall shear stress in aneurysmal flow
A. Arzani and S. C. Shadden · 2016
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Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression
A. Bakhshinejad, A. Baghaie, A. Vali, D. Saloner, V. L. Rayz, and R. M. D’Souza · 2017
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P. Ramachandran, B. Zoph, and Q. V. Le · 2017
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Wall shear stress fixed points in cardiovascular fluid mechanics
A. Arzani and S. C. Shadden · 2018
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M. Raffel, C. E. Willert, F. Scarano, C. J. Kähler, S. T. Wereley, and J. Kompenhans · 2018
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Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?
A. Arzani · 2018
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Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
K. Manohar, B. W. Brunton, J. N. Kutz, and S. L. Brunton · 2018
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Transient flow prediction in an idealized aneurysm geometry using data assimilation
F. Gaidzik, D. Stucht, C. Roloff, O. Speck, D. Thévenin, and G. Janiga · 2019
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Variational data assimilation for transient blood flow simulations: Cerebral aneurysms as an illustrative example
S. W. Funke, M. Nordaas, Ø. Evju, M. S. Alnæs, and K. A. Mardal · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Data-driven prediction of unsteady flow over a circular cylinder using deep learning
S. Lee and D. You · 2019
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Finite element modeling of near-wall mass transport in cardiovascular flows
K. B. Hansen, A. Arzani, and S. C. Shadden · 2019
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The story of wall shear stress in coronary artery atherosclerosis: biochemical transport and mechanotransduction
M. Mahmoudi, A. Farghadan, D. R. McConnell, A. J. Barker, J. J. Wentzel, M. J. Budoff, and A. Arzani · 2020
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Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks
B. Feiger, J. Gounley, D. Adler, J. A. Leopold, E. W. Draeger, R. Chaudhury, J. Ryan, G. Pathangey, K. Winarta, D. Frakes, F. Michor, and A. Randles · 2020
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Data-driven aerospace engineering: Reframing the industry with machine learning
S. L. Brunton, J. N. Kutz, K. Manohar, A. Y. Aravkin, et al · 2020
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When and why PINNs fail to train: A neural tangent kernel perspective
S. Wang, X. Yu, and P. Perdikaris · 2020
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Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data
L. Sun and J. X. Wang · 2020
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A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
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Comparison of statistical learning approaches for cerebral aneurysm rupture assessment
F. J. Detmer, D. Lückehe, F. Mut, M. Slawski, S. Hirsch, P. Bijlenga, G. von Voigt, and J. R. Cebral · 2020
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Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry
H. Wang, Z. Yang, B. Li, and S. Wang · 2020
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The neural particle method–an updated Lagrangian physics informed neural network for computational fluid dynamics
H. Wessels, C. Weißenfels, and P. Wriggers · 2020
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
L. Sun, H. Gao, S. Pan, and J. X. Wang · 2020
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M. Raissi, A. Yazdani, and G. E. Karniadakis · 2020
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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
G. Kissas, Y. Yang, E. Hwuang, W. R. Witschey, J. A. Detre, and P. Perdikaris · 2020
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Learning unknown physics of non-Newtonian fluids
B. Reyes, A. A. Howard, P. Perdikaris, and A. M. Tartakovsky · 2020
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X. Meng and G. E. Karniadakis · 2020
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Application of physics-based flow models in cardiovascular medicine: Current practices and challenges
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Wall shear stress topological skeleton analysis in cardiovascular flows: Methods and applications
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Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks
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From coarse wall measurements to turbulent velocity fields with deep learning
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Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
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Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis
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Deep neural network-based strategy for optimal sensor placement in data assimilation of turbulent flow
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PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
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