arXiv Machine Learning By Yunbei Pan, Jiahang Sha, Simon A. Lee, Maxime Cannesson, Wei Wang, Jeffrey N. Chiang

HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference

Read the original on arXiv Machine Learning →

arXiv:2608. 10011v1 Announce Type: cross Abstract: Continuous hemodynamic monitoring guides treatment decisions in surgery and intensive care.

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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.

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arXiv Machine Learning
Jun 10

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv:2606. 11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation.

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arXiv Machine Learning
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Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework

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

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arXiv Machine Learning
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Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets

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