A physics-informed neural network model hosted on HuggingFace, with name suggesting relevance to MRI imaging and SIREN architecture; detailed information requires consulting the official page.
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
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
This paper introduces a Bayesian surrogate-modeling framework for inferring regional Holzapfel-Ogden material parameters in a left ventricle partitioned into five physiological zones, using cardiac MRI data to overcome the limitation of treating the myocardium as mechanically homogeneous.
See LICENSE fileCardiac BiomechanicsBayesian InferenceSurrogate Modeling
Evaluates six sleep staging foundation models under reduced PSG channel conditions, providing insights for lightweight sensing solutions in long-term home monitoring.
See LICENSE fileSleep AnalysisPSGChannel Reduction
This study develops a patient-specific image-based computational modeling framework to evaluate the hemodynamic impact of mitral and aortic valve repair in five LVAD-supported patients, addressing valve dysfunction threats to long-term LVAD therapy success.
RFlash uses differentiable acoustic simulation and radiance field decomposition to reduce bone-induced shadow artifacts in ultrasound imaging, improving symmetric assessment of fetal brain hemispheres.
A region-held-out evaluation protocol for hydrogen embrittlement detection in 316L stainless steel, preventing data leakage from same specimen regions, comparing texture features vs deep learning for SEM micrograph classification.
