Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm
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arXiv:2608. 13629v1 Announce Type: cross Abstract: Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or unfavorable evolution in Abdominal Aortic Aneurysms (AAA).
arXiv:2606. 06313v1 Announce Type: cross Abstract: Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradients.
arXiv:2509. 15900v2 Announce Type: replace-cross Abstract: This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models.
arXiv:2609.23826v1 Announce Type: new Abstract: Mitral regurgitation is the most common heart valve disorder worldwide, affecting over 2% of the global population, rising to at least 10% in adults ov...
arXiv:2602. 23035v2 Announce Type: replace Abstract: Cardiac blood flow patterns contain rich information about disease severity and clinical interventions, yet current imaging and computational methods fail to capture underlying relational structures of coherent flow features.
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.