Fetching the paper…
Reading the bibliography…
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning.
Zhu, Y., Zabaras, N., Koutsourelakis, P.S., Perdikaris, P., 2019 · 1901
Earlier work this paper cites.
Spin warp NMR imaging and applications to human whole-body imaging
Edelstein, W.A., Hutchison, J.M., Johnson, G., Redpath, T., 1980 · 1980
Earlier work this paper cites.
Noninvasive transcranial doppler ultrasound recording of flow velocity in basal cerebral arteries
Aaslid, R., Markwalder, T.M., Nornes, H., 1982 · 1982
Earlier work this paper cites.
Cardiovascular survey methods.. volume 56
Rose, G.A., Blackburn, H., Gillum, R., Prineas, R., et al., 1982 · 1982
Earlier work this paper cites.
Non-invasive magnetic stimulation of human motor cortex
Barker, A.T., Jalinous, R., Freeston, I.L., 1985 · 1985
Earlier work this paper cites.
Large sample properties of simulations using latin hypercube sampling
Stein, M., 1987 · 1987
Earlier work this paper cites.
Multiscale modeling of spatially variable water and energy balance processes
Famiglietti, J., Wood, E., 1994 · 1994
Earlier work this paper cites.
Mechanical principles in arterial disease
O’Rourke, M., 1995 · 1995
Earlier work this paper cites.
Estimation of systemic vascular bed parameters for artificial heart control
Yu, Y.C., Boston, J.R., Simaan, M.A., Antaki, J.F., 1998 · 1998
Earlier work this paper cites.
Structured tree outflow condition for blood flow in larger systemic arteries
Olufsen, M.S., 1999 · 1999
Earlier work this paper cites.
Physics of the human cardiovascular system
Stefanovska, A., 1999 · 1999
Earlier work this paper cites.
Steady-state free precession magnetic resonance imaging of the heart: comparison with segmented k-space gradient-echo imaging
Plein, S., Bloomer, T.N., Ridgway, J.P., Jones, T.R., Bainbridge, G.J., Sivananthan, M.U., 2001 · 2001
Earlier work this paper cites.
Principles of computerized tomographic imaging
Kak, A.C., Slaney, M., Wang, G., 2002 · 2002
Earlier work this paper cites.
Adverse cerebral events detected after subarachnoid hemorrhage using brain oxygen and microdialysis probes
Kett-White, R., Hutchinson, P.J., Al-Rawi, P.G., Gupta, A.K., Pickard, J.D., Kirkpatrick, P.J., 2002 · 2002
Earlier work this paper cites.
One dimensional and multiscale models for blood flow circulation
Lamponi, D., 2004 · 2004
Earlier work this paper cites.
Gaussian Processes in Machine Learning. Springer Berlin Heidelberg, Berlin, Heidelberg
Rasmussen, C.E., 2004 · 2004
Earlier work this paper cites.
A coupled momentum method for modeling blood flow in three-dimensional deformable arteries
Figueroa, C.A., Vignon-Clementel, I.E., Jansen, K.E., Hughes, T.J., Taylor, C.A., 2006 · 2006
Earlier work this paper cites.
Multidimensional modelling for the carotid artery blood flow
Urquiza, S., Blanco, P., Vénere, M., Feijóo, R., 2006 · 2006
Earlier work this paper cites.
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., 2006 · 2006
Earlier work this paper cites.
A hybrid body sensor network for continuous and long-term measurement of arterial blood pressure, in: 2007 4th IEEE/EMBS International Summer School and Symposium on Medical Devices and Biosensors, IEEE. pp. 121–123
Chan, C., Poon, C., Wong, R.C., Zhang, Y., 2007 · 2007
Earlier work this paper cites.
Pulse wave propagation in a model human arterial network: assessment of 1-D numerical simulations against in vitro measurements
Matthys, K.S., Alastruey, J., Peiró, J., Khir, A.W., Segers, P., Verdonck, P.R., Parker, K.H., Sherwin, S.J., 2007 · 2007
Earlier work this paper cites.
Morphometry-based impedance boundary conditions for patient-specific modeling of blood flow in pulmonary arteries
Spilker, R.L., Feinstein, J.A., Parker, D.W., Reddy, V.M., Taylor, C.A., 2007 · 2007
Earlier work this paper cites.
Reduced modelling of blood flow in the cerebral circulation: Coupling 1-D, 0-D and cerebral auto-regulation models
Alastruey, J., Moore, S., Parker, K., David, T., Peiró, J., Sherwin, S., 2008 · 2008
Cited alongside, same era.
An image-based modeling framework for patient-specific computational hemodynamics
Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., Steinman, D.A., 2008 · 2008
Cited alongside, same era.
Outflow boundary conditions for arterial networks with multiple outlets
Grinberg, L., Karniadakis, G.E., 2008 · 2008
Cited alongside, same era.
Validation of a one-dimensional model of the systemic arterial tree
Reymond, P., Merenda, F., Perren, F., Rufenacht, D., Stergiopulos, N., 2009 · 2009
Cited alongside, same era.
The arterial Windkessel
Westerhof, N., Lankhaar, J.W., Westerhof, B.E., 2009 · 2009
Cited alongside, same era.
Cardiovascular Mathematics: Modeling and simulation of the circulatory system. volume 1
A methodological paradigm for patient-specific multi-scale CFD simulations: from clinical measurements to parameter estimates for individual analysis
Pant, S., Fabrèges, B., Gerbeau, J.F., Vignon-Clementel, I., 2014 · 2014
Later among the works it cites.
Fractional-order viscoelasticity in one-dimensional blood flow models
Perdikaris, P., Karniadakis, G.E., 2014 · 2014
Later among the works it cites.
A systematic comparison between 1-d and 3-d hemodynamics in compliant arterial models
Xiao, N., Alastruey, J., Alberto Figueroa, C., 2014 · 2014
Later among the works it cites.
Closed-loop cardiovascular system model and partial hepatectomy simulation
Audebert, C., Bucur, P., Vibert, E., Gerbeau, J.F., Vignon-Clementel, I., 2015 · 2015
Later among the works it cites.
An effective fractal-tree closure model for simulating blood flow in large arterial networks
Perdikaris, P., Grinberg, L., Karniadakis, G.E., 2015 · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Formaggia, L., Quarteroni, A., Veneziani, A., 2010 · 2010
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256
Glorot, X., Bengio, Y., 2010 · 2010
Cited alongside, same era.
Tuning multidomain hemodynamic simulations to match physiological measurements
Spilker, R.L., Taylor, C.A., 2010 · 2010
Cited alongside, same era.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., Singer, Y., 2011 · 2011
Cited alongside, same era.
Modeling blood flow circulation in intracranial arterial networks: a comparative 3D/1D simulation study
Grinberg, L., Cheever, E., Anor, T., Madsen, J.R., Karniadakis, G., 2011 · 2011
Cited alongside, same era.
Arterial stiffness, pressure and flow pulsatility and brain structure and function: the Age, Gene/Environment Susceptibility–Reykjavik study
Mitchell, G.F., van Buchem, M.A., Sigurdsson, S., Gotal, J.D., Jonsdottir, M.K., Kjartansson, Ó., Garcia, M., Aspelund, T., Harris, T.B., Gudnason, V., et al., 2011 · 2011
Cited alongside, same era.
Validation of a patient-specific one-dimensional model of the systemic arterial tree
Reymond, P., Bohraus, Y., Perren, F., Lazeyras, F., Stergiopulos, N., 2011 · 2011
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning., in: OSDI, pp. 265–283
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al., 2016 · 2016
Later among the works it cites.
Seg3D: Volumetric Image Segmentation and Visualization. Scientific Computing and Imaging Institute (SCI), Download from: http://www.seg3d.org
CIBC, 2016 · 2016
Later among the works it cites.
An overview of gradient descent optimization algorithms
Ruder, S., 2016 · 2016
Later among the works it cites.
Simple and scalable predictive uncertainty estimation using deep ensembles, in: Advances in Neural Information Processing Systems, pp. 6402–6413
Lakshminarayanan, B., Pritzel, A., Blundell, C., 2017 · 2017
Later among the works it cites.
Automatic differentiation in Pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A., 2017 · 2017
Later among the works it cites.
Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty
Schiavazzi, D.E., Baretta, A., Pennati, G., Hsia, T.Y., Marsden, A.L., 2017 · 2017
Later among the works it cites.
Optimization of topological complexity for one-dimensional arterial blood flow models
Fossan, F.E., Mariscal-Harana, J., Alastruey, J., Hellevik, L.R., 2018 · 2018
Later among the works it cites.
Relation between blood pressure and pulse wave velocity for human arteries
Ma, Y., Choi, J., Hourlier-Fargette, A., Xue, Y., Chung, H.U., Lee, J.Y., Wang, X., Xie, Z., Kang, D., Wang, H., et al., 2018 · 2018
Later among the works it cites.
Randomized prior functions for deep reinforcement learning, in: Advances in Neural Information Processing Systems, pp. 8617–8629
Osband, I., Aslanides, J., Cassirer, A., 2018 · 2018
Later among the works it cites.
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Rudy, S.H., Kutz, J.N., Brunton, S.L., 2018 · 2018
Later among the works it cites.
Learning parameters and constitutive relationships with physics informed deep neural networks
Tartakovsky, A.M., Marrero, C.O., Tartakovsky, D., Barajas-Solano, D., 2018 · 2018
Later among the works it cites.
Physics-informed deep generative models
Yang, Y., Perdikaris, P., 2018 · 2018
Later among the works it cites.
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Zhu, Y., Zabaras, N., 2018 · 2018
Later among the works it cites.
A one-dimensional hemodynamic model of the coronary arterial tree
Duanmu, Z., Chen, W., Gao, H., Yang, X., Luo, X., Hill, N.A., 2019 · 2019
Closest in time.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Raissi, M., Perdikaris, P., Karniadakis, G., 2019 · 2019
Closest in time.
Adversarial uncertainty quantification in physics-informed neural networks
Yang, Y., Perdikaris, P., 2019 · 2019
Closest in time.