Uncovering near-wall blood flow from sparse data with physics-informed neural networks-Reference-Cited by-同舟云学术

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Uncovering near-wall blood flow from sparse data with physics-informed neural networks

Published:2021-07 Issue:7 Volume:33 Page:071905
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Arzani Amirhossein¹^ORCID,Wang Jian-Xun²^ORCID,D'Souza Roshan M.³

Affiliation:

1. Department of Mechanical Engineering, Northern Arizona University, Flagstaff, Arizona 86011, USA

2. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA

3. Department of Mechanical Engineering, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin 53211, USA

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0055600

Reference57 articles.

1. Application of physics-based flow models in cardiovascular medicine: Current practices and challenges

2. Lagrangian wall shear stress structures and near-wall transport in high-Schmidt-number aneurysmal flows

3. The Story of Wall Shear Stress in Coronary Artery Atherosclerosis: Biochemical Transport and Mechanotransduction

4. Coronary Artery Wall Shear Stress Is Associated With Progression and Transformation of Atherosclerotic Plaque and Arterial Remodeling in Patients With Coronary Artery Disease

5. Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

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