scholarly journals Possibilities of using Neural Networks to Blood Flow Modelling

2021 ◽  
Author(s):  
Katarína Buzáková ◽  
Katarína Bachratá ◽  
Hynek Bachratý ◽  
Michal Chovanec
Keyword(s):  
Neural Networks ◽  
Blood Flow ◽  
Flow Modelling

scholarly journals A benchmark study of numerical schemes for one-dimensional arterial blood flow modelling

2015 ◽  
Vol 31 (10) ◽  
pp. e02732 ◽  
Author(s):  
Etienne Boileau ◽  
Perumal Nithiarasu ◽  
Pablo J. Blanco ◽  
Lucas O. Müller ◽  
Fredrik Eikeland Fossan ◽  
...  
Keyword(s):  
Blood Flow ◽  
Arterial Blood Flow ◽  
Numerical Schemes ◽  
One Dimensional ◽  
Flow Modelling ◽  
Benchmark Study ◽  
Arterial Blood

Use of Neural Networks for Modelling of Passenger Dynamics in Airport Terminal Environment

2014 ◽  
Vol 708 ◽  
pp. 107-112
Author(s):  
Pavlína Hlavsová ◽  
Jaromír Široký
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Flow Control ◽  
Modelling And Simulation ◽  
Mathematics Methods ◽  
Flow Modelling ◽  
Passenger Flow ◽  
Airport Terminal ◽  
Central Nervous Systems ◽  
Modern Mathematics

Neural networks are methods inspired by animals´ central nervous systems, particularly by the human brain. As one of the modern mathematics methods, neural networks have been used to solve a wide variety of both practical and theoretical tasks. The aim of this paper is to illustrate the use of neural networks for modelling of passenger dynamics in the airport terminal environment. This model could be used for passenger flow control, since for the management to be appropriate it should involve passenger dynamics prediction for effective and accurate passenger flow modelling and simulation.

1D AND 3D BLOOD FLOW MODELLING FOR PATIENT SPECIFIC CEREBRAL VASCULATURE AND ANEURYSM

2008 ◽  
Vol 41 ◽  
pp. S8 ◽  
Author(s):  
Harvey Ho ◽  
Stuart Norris ◽  
Kumar Mithraratne ◽  
Peter Hunter
Keyword(s):  
Blood Flow ◽  
Patient Specific ◽  
Cerebral Vasculature ◽  
Flow Modelling

scholarly journals Erratum to the paper “Methods of Blood Flow Modelling”

2015 ◽  
Vol 11 (1) ◽  
pp. 91-91
Author(s):  
N. Bessonov ◽  
A. Sequeira ◽  
S. Simakov ◽  
Yu. Vassilevski ◽  
V. Volpert
Keyword(s):  
Blood Flow ◽  
Flow Modelling

scholarly journals Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

2021 ◽  
Vol 118 (13) ◽  
pp. e2100697118
Author(s):  
Shengze Cai ◽  
He Li ◽  
Fuyin Zheng ◽  
Fang Kong ◽  
Ming Dao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Neural Networks ◽  
Blood Flow ◽  
Vision Loss ◽  
Three Dimensional ◽  
Flow Fields ◽  
Diabetic Patients ◽  
Imaging Data ◽  
2D Images

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro methods based on traditional microfluidic platforms have been developed to mimic physiological conditions. However, existing methods lack the capability to provide accurate assessment of these flow fields, particularly in vessels with complex geometries. Conventional approaches to quantify flow fields rely either on analyzing only visual images or on enforcing underlying physics without considering visualization data, which could compromise accuracy of predictions. Here, we present artificial-intelligence velocimetry (AIV) to quantify velocity and stress fields of blood flow by integrating the imaging data with underlying physics using physics-informed neural networks. We demonstrate the capability of AIV by quantifying hemodynamics in microchannels designed to mimic saccular-shaped microaneurysms (microaneurysm-on-a-chip, or MAOAC), which signify common manifestations of diabetic retinopathy, a leading cause of vision loss from blood-vessel damage in the retina in diabetic patients. We show that AIV can, without any a priori knowledge of the inlet and outlet boundary conditions, infer the two-dimensional (2D) flow fields from a sequence of 2D images of blood flow in MAOAC, but also can infer three-dimensional (3D) flow fields using only 2D images, thanks to the encoded physics laws. AIV provides a unique paradigm that seamlessly integrates images, experimental data, and underlying physics using neural networks to automatically analyze experimental data and infer key hemodynamic indicators that assess vascular injury.

scholarly journals Data-driven cardiovascular flow modelling: examples and opportunities

2021 ◽  
Vol 18 (175) ◽  
pp. 20200802
Author(s):  
Amirhossein Arzani ◽  
Scott T. M. Dawson
Keyword(s):  
Blood Flow ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Low Rank ◽  
Patient Specific ◽  
Dynamic Mode ◽  
Cardiovascular Research ◽  
Flow Modelling ◽  
Modelling Techniques ◽  
Cardiovascular Flow

High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

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