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We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations.
Carotid bifurcation atherosclerosis. quantitative correlation of plaque localization with flow velocity profiles and wall shear stress.,
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Prognostic accuracy of cerebral blood flow measurement by perfusion computed tomography, at the time of emergency room admission, in acute stroke patients,
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Default-mode network activity distinguishes alzheimer’s disease from healthy aging: evidence from functional mri,
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Inflammation, atherosclerosis, and coronary artery disease,
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Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (mri) and functional mri,
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Planar laser induced fluorescence in aqueous flows,
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Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter accuracy (assessment by coronary computed tomographic angiography of individuals undergoing invasive coronary angiography) trial,
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Outflow boundary conditions for arterial networks with multiple outlets,
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Phase-contrast magnetic resonance imaging measurements in intracranial aneurysms in vivo of flow patterns, velocity fields, and wall shear stress: comparison with computational fluid dynamics,
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High-speed label-free functional photoacoustic microscopy of mouse brain in action,
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Deep learning in drug discovery,
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Deep multi-fidelity Gaussian processes,
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems,
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On the expressive power of deep neural networks,
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Coronary atherosclerosis imaging by coronary ct angiography: current status, correlation with intravascular interrogation and meta-analysis,
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Imagenet classification with deep convolutional neural networks,
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A convergence study of a new partitioned fluid–structure interaction algorithm based on fictitious mass and damping,
H. Baek, G. E. Karniadakis, · 2012
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G. Karniadakis, S. Sherwin, Spectral/hp element methods for computational fluid dynamics, Oxford University Press, 2013
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Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
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Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning,
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Low speed wind tunnel testing,
J. B. Barlow, W. H. Rae Jr, A. Pope, · 2015
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Automatic differentiation in machine learning: a survey,
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Taking the human out of the loop: A review of bayesian optimization,
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. De Freitas, · 2016
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Parametric gaussian process regression for big data,
M. Raissi, · 2017
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling,
P. Perdikaris, M. Raissi, A. Damianou, N. Lawrence, G. E. Karniadakis, · 2017
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Automatic differentiation in pytorch (2017)
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Hidden physics models: Machine learning of nonlinear partial differential equations,
M. Raissi, G. E. Karniadakis, · 2018
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Neural ordinary differential equations,
T. Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, · 2018
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Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-newtonian flows,
L. Zhao, Z. Li, B. Caswell, J. Ouyang, G. E. Karniadakis, · 2018
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