arXiv AI By Adri\'an Robles Arques, Mart\'in Ruiz Fernandez, Javier Sanchis, Miguel A. Teruel, Juan Trujillo

Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm

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arXiv Machine Learning
Aug 17

Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets

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).

By Oscar L. Cruz-Gonzalez, Val\'erie Deplano, Badih Ghattas
arXiv Machine Learning
Jun 5

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

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.

By Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani
arXiv Machine Learning
Aug 12

Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements

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.

By Viraj Patel, Marko Grujic, Philipp Aigner, Theodor Abart, Marcus Granegger, Deblina Bhattacharjee, Katharine Fraser
arXiv Machine Learning
4d ago

A Physics-Conditioned Neural Operator for Generalization of Atrioventricular Valve Mechanics across Pressure and Tissue Properties

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.

By Shawn Koohy, Wensi Wu, Matthew A Jolley, Paris Perdikaris