Machine learning for cardiovascular biomechanics modeling: challenges and beyond
Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …
have provided new research opportunities in cardiovascular modeling. While classifying …
[HTML][HTML] 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 …
Prediction of water transport properties on an anisotropic wetting surface via deep learning
Understanding the water flow behavior on an anisotropic wetting surface is of practical
significance in nanofluidic devices for their performance improvement. However, current …
significance in nanofluidic devices for their performance improvement. However, current …
[HTML][HTML] Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage
Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable
insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and …
insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and …
A physics-guided deep learning approach for functional assessment of cardiovascular disease in IoT-based smart health
The rapid development of the Internet of Things (IoT) widely supports the smart healthcare
system. IoT-based smart health has significant importance for the diagnosis of …
system. IoT-based smart health has significant importance for the diagnosis of …
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 …
Liquid-vapor two-phase flow in centrifugal pump: Cavitation, mass transfer, and impeller structure optimization
Computational fluid dynamics (CFD) has been widely used to model the internal flow field of
centrifugal pumps to analyze cavitation phenomena. However, accurate determination of …
centrifugal pumps to analyze cavitation phenomena. However, accurate determination of …
Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane …
In this study, data-driven deep learning methods were applied in order to model and predict
the treatment of real municipal wastewater using anaerobic membrane bioreactors …
the treatment of real municipal wastewater using anaerobic membrane bioreactors …
Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization
around the world. Recent technological advances have facilitated analyzing, visualizing …
around the world. Recent technological advances have facilitated analyzing, visualizing …
Prediction of the adsorption properties of liquid at solid surfaces with molecular scale surface roughness via encoding-decoding convolutional neural networks
Molecular dynamics (MD) simulation can effectively analyze the transport properties of liquid
at the solid surface with different nanoscale roughness, while high computational costs are …
at the solid surface with different nanoscale roughness, while high computational costs are …