PINN inverse solver reconstructs arterial hemodynamics from minimal cuff data, tunes terminal resistance and compliance, trains in 4000 iterations, and achieves r=0.95 correlation for central systolic pressure on clinical data.
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A neural surrogate trained on a clinically-derived virtual cohort enables real-time hemodynamic prediction and cardiac output estimation while rejecting non-physiological parameter sets.
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Fast and Accurate Inverse Blood Flow Modeling from Minimal Cuff-Pressure Data via PINNs
PINN inverse solver reconstructs arterial hemodynamics from minimal cuff data, tunes terminal resistance and compliance, trains in 4000 iterations, and achieves r=0.95 correlation for central systolic pressure on clinical data.
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Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
A neural surrogate trained on a clinically-derived virtual cohort enables real-time hemodynamic prediction and cardiac output estimation while rejecting non-physiological parameter sets.