Mask-Morph Graph U-Net morphs coarse graph hierarchies with barycentric parameterization and applies masked supervised pretraining to improve generalizability of hierarchical GNN surrogates for crashworthiness prediction on variable meshes.
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Formulates active learning sample acquisition for surrogate model-based reliability analysis as multi-objective optimization yielding a Pareto set, with adaptive selection rules that show robust performance across tested limit-state functions.
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.
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.