[HTML][HTML] Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics
Optimization and uncertainty quantification have been playing an increasingly important role
in computational hemodynamics. However, existing methods based on principled modeling …
in computational hemodynamics. However, existing methods based on principled modeling …
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and
temporally discretization of the governing equation using polynomials into a finite …
temporally discretization of the governing equation using polynomials into a finite …
A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations
Image-based computational fluid dynamics (CFD) modeling enables derivation of
hemodynamic information (eg, flow field, wall shear stress, and pressure distribution), which …
hemodynamic information (eg, flow field, wall shear stress, and pressure distribution), which …
A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta
Numerical analysis methods including finite element analysis (FEA), computational fluid
dynamics (CFD), and fluid–structure interaction (FSI) analysis have been used to study the …
dynamics (CFD), and fluid–structure interaction (FSI) analysis have been used to study the …
Deep learning based centerline-aggregated aortic hemodynamics: an efficient alternative to numerical modeling of hemodynamics
P Yevtushenko, L Goubergrits… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Image-based patient-specific modelling of hemodynamics are gaining increased popularity
as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases …
as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases …
Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
E Pajaziti, J Montalt-Tordera, C Capelli… - PLoS Computational …, 2023 - journals.plos.org
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and
analyse potential treatment options. CFD has shown to be beneficial in improving patient …
analyse potential treatment options. CFD has shown to be beneficial in improving patient …
Deep learning for computational hemodynamics: A brief review of recent advances
A Taebi - Fluids, 2022 - mdpi.com
Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better
understanding various medical conditions, designing more effective drug delivery systems …
understanding various medical conditions, designing more effective drug delivery systems …
Adjoint-based inverse analysis of windkessel parameters for patient-specific vascular models
A human circulatory system is composed of more than 50,000 miles of blood vessels. Such a
huge network of vessels is responsible for the elevated pressure values within large arteries …
huge network of vessels is responsible for the elevated pressure values within large arteries …
A parameter estimation framework for patient-specific hemodynamic computations
We propose a fully automated parameter estimation framework for performing patient-
specific hemodynamic computations in arterial models. To determine the personalized …
specific hemodynamic computations in arterial models. To determine the personalized …
Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: an investigation of optimal framework based on vascular morphology
X Zhang, B Mao, Y Che, J Kang, M Luo, A Qiao… - Computers in Biology …, 2023 - Elsevier
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …