Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

DOI10.1016/j.cma.2019.112623zbMath1441.76149arXiv1905.04817OpenAlexW2973886134WikidataQ114671789 ScholiaQ114671789MaRDI QIDQ1989082

Georgios Kissas, Paris Perdikaris, Yibo Yang, John A. Detre, Eileen Hwuang, Walter R. Witschey

Publication date: 24 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.04817

zbMATH Keywords

blood flow modelingpulse wave propagationnon-invasive diagnosticsdeep neural networksdata-driven modeling

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) Physiological flows (76Z05) Physiological flow (92C35)

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Uses Software

• GAUSSIAN
• BayesDA
• TensorFlow
• Seg3D
• PyTorch
• AdaGrad
• Adam

Cites Work

• Unnamed Item
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• Large Sample Properties of Simulations Using Latin Hypercube Sampling
• Computational modelling of 1D blood flow with variable mechanical properties and its application to the simulation of wave propagation in the human arterial system
• Advanced Lectures on Machine Learning
• Reduced modelling of blood flow in the cerebral circulation: Coupling 1‐D, 0‐D and cerebral auto‐regulation models

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