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The paper presents a Physics‑Conditioned Neural Operator (PCNO), a transformer‑based model that predicts mitral and tricuspid valve leaflet displacement, strain, and stress fields conditioned on systolic blood pressure and tissue properties. Trained on FEBio finite‑element simulations, PCNO achieves a displacement error of 4.48 % and maintains mean errors of unsupervised geometric measures within 3.5 % even when pressure and material parameters lie outside the training set, demonstrating a conditioned solution operator rather than simple interpolation. Compared to graph neural network baselines, PCNO shows superior accuracy, particularly in stress predictions.
arXiv:2609.15104v1 Announce Type: cross Abstract: We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodyna...
The paper presents a Deep Operator Network that reconstructs full‑field 4D mitral regurgitation hemodynamics from sparse planar velocity data and boundary pressure traces. The network is pretrained on a URANS database of eleven orifice phantoms and then fine‑tuned on new cases, achieving rapid predictions in minutes. While the adaptation improves flow topology in the observed plane, reconstruction error increases sharply with distance from that plane, limiting physical consistency in the surrounding volume.
arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework comb...
arXiv:2608. 10011v1 Announce Type: cross Abstract: Continuous hemodynamic monitoring guides treatment decisions in surgery and intensive care.
arXiv:2607. 29243v1 Announce Type: cross Abstract: Computed tomography angiography (CTA) is crucial for preprocedural TAVI planning, providing the anatomical information required for prosthesis sizing and vascular access assessment.