Presents physics-informed hemodynamic modeling for coronary intervention, enabling data-free prediction and sparse-data assimilation to overcome limitations of traditional numerical analysis and purely data-driven approaches.
See LICENSE fileHemodynamicsCoronary InterventionPhysics-Informed Neural Networks
Neural networks predict 0D reduced-order model parameters from geometry, cutting cardiovascular flow error by over half and running complex pulmonary cases in under 2 seconds on a laptop.
arXiv preprint (copyright held by authors)HemodynamicsReduced OrderDigital Twin
HIPNO leverages symmetry-aware physics-informed neural operators to infer hemodynamic states from non-invasive signals, addressing scale symmetry challenges in physics-informed hemodynamic inference and expanding access to advanced monitoring.
See LICENSE fileHemodynamicsPhysics-Informed Neural NetworksNeural Operators
