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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Hybrid mesh GNNs with geometry-aware attention achieve 3.20 mm temporal RMSE on a 25-sample full-vehicle lateral pole-impact test set while preserving interpretable displacement fields.
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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.
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Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention
Hybrid mesh GNNs with geometry-aware attention achieve 3.20 mm temporal RMSE on a 25-sample full-vehicle lateral pole-impact test set while preserving interpretable displacement fields.