Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
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Abstract
No abstract available for this paper.
Citation impact
620
total citations
- FWCI
- 36.82
- Percentile
- 100%
- References
- 86
Citations per year
Authors
6Topics & keywords
Topics
Keywords
- Pulsatile flow
- Artificial neural network
- Computer science
- Artificial intelligence
- Calibration
- Machine learning
- Pipeline (software)
- Flow (mathematics)
UN Sustainable Development Goals
- Life below water
No related works found for this paper.
Funding
- NSNational Science FoundationAwards: 1321851, DGE-1321851, HR00111890034
- UDU.S. Department of Energy
- DADefense Advanced Research Projects Agency
- ARAdvanced Research Projects Agency
- NINational Institute of Biomedical Imaging and Bioengineering
- NINational Institute of Child Health and Human Development
- ASAdvanced Scientific Computing Research
- EKEunice Kennedy Shriver National Institute of Child Health and Human Development