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 …
Data-driven cardiovascular flow modelling: examples and opportunities
A Arzani, STM Dawson - Journal of the Royal Society …, 2021 - royalsocietypublishing.org
High-fidelity blood flow modelling is crucial for enhancing our understanding of
cardiovascular disease. Despite significant advances in computational and experimental …
cardiovascular disease. Despite significant advances in computational and experimental …
Special issue on machine learning and data-driven methods in fluid dynamics
Machine learning (ie, modern data-driven optimization and applied regression) is a rapidly
growing field of research that is having a profound impact across many fields of science and …
growing field of research that is having a profound impact across many fields of science and …
Integrating multi-fidelity blood flow data with reduced-order data assimilation
M Habibi, RM D'Souza, STM Dawson… - Computers in Biology and …, 2021 - Elsevier
High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is
challenging. Direct blood flow measurement inside the body with in-vivo measurement …
challenging. Direct blood flow measurement inside the body with in-vivo measurement …
A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced‐order model
Y Zhou, Y He, J Wu, C Cui, M Chen… - International Journal for …, 2022 - Wiley Online Library
Precise model personalization is a key step towards the application of cardiovascular
physical models. In this manuscript, we propose to use deep learning (DL) to solve the …
physical models. In this manuscript, we propose to use deep learning (DL) to solve the …
An optimal control approach to determine resistance‐type boundary conditions from in‐vivo data for cardiovascular simulations
The choice of appropriate boundary conditions is a fundamental step in computational fluid
dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact …
dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact …
[HTML][HTML] Personalised parameter estimation of the cardiovascular system: Leveraging data assimilation and sensitivity analysis
Detailed models of dynamical systems used in the life sciences may include hundreds of
state variables and many input parameters, often with physical meanings. Therefore …
state variables and many input parameters, often with physical meanings. Therefore …
New perspectives on sensitivity and identifiability analysis using the unscented kalman filter
Detailed dynamical systems' models used in the life sciences may include hundreds of state
variables and many input parameters, often with physical meaning. Therefore, efficient and …
variables and many input parameters, often with physical meaning. Therefore, efficient and …
Data assimilation by stochastic ensemble kalman filtering to enhance turbulent cardiovascular flow data from under-resolved observations
D De Marinis, D Obrist - Frontiers in cardiovascular medicine, 2021 - frontiersin.org
We propose a data assimilation methodology that can be used to enhance the spatial and
temporal resolution of voxel-based data as it may be obtained from biomedical imaging …
temporal resolution of voxel-based data as it may be obtained from biomedical imaging …
Parameter estimation tools for cardiovascular flow modeling of fetal circulation
Usually, clinicians assess the correct hemodynamic behavior and fetal well-being during the
gestational age thanks to their professional expertise, with the support of some indices …
gestational age thanks to their professional expertise, with the support of some indices …