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

Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networ...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 358; H. C; S. 112623
Hauptverfasser:	Kissas, Georgios, Yang, Yibo, Hwuang, Eileen, Witschey, Walter R., Detre, John A., Perdikaris, Paris
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.01.2020 Elsevier BV Elsevier
Schlagworte:	Aorta Artificial intelligence Artificial neural networks Bifurcations Blood flow modeling Blood pressure Calibration Computer simulation Computing costs Data-driven modeling Deep neural networks Machine learning Measurement techniques Neural networks Non-invasive diagnostics One dimensional models Parameter estimation Post-processing Prediction models Principles Propagation Pulse propagation Pulse wave propagation Simulators Stress propagation Topology Wall shear stresses Wave propagation Data-driven modeling Pulse wave propagation Non-invasive diagnostics Deep neural networks Blood flow modeling
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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